You searched for subject:(Recommendation systems).
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University of Minnesota
1.
Christakopoulou, Evangelia.
Improving the Quality of Top-N Recommendation.
Degree: PhD, Computer Science, 2018, University of Minnesota
URL: http://hdl.handle.net/11299/195398 
► Top-N recommenders are systems that provide a ranked list of N products to every user; the recommendations are of items that the user will potentially…
(more)
▼ Top-N recommenders are systems that provide a ranked list of N products to every user; the recommendations are of items that the user will potentially like. Top-N recommendation systems are present everywhere and used by millions of users, as they enable them to quickly find items they are interested in, without having to browse or search through big datasets; an often impossible task. The quality of the recommendations is crucial, as it determines the usefulness of the recommender to the users. So, how do we decide which products should be recommended? Also, how do we address the limitations of current approaches, in order to achieve better quality? In order to provide insight into these problems, this thesis focuses on developing novel, scalable algorithms that improve the state-of-the-art top-N recommendation quality, while providing insight into the top-N recommendation task. The developed algorithms address some of the limitations of existent top-N recommendation approaches and can be applied to real-world problems and datasets. The main areas of our contributions are the following: 1. Exploiting higher-order sets of items: We investigate to what extent higher-order sets of items are present in real-world datasets, beyond pairs of items. We also show how to best utilize them to improve the top-N recommendation quality. 2. Estimating a global and multiple local models: We show that estimating multiple user-subset specific local models, beyond a global model significantly improves the top-N recommendation quality. We demonstrate this with both item-item models and latent space models. 3. Investigating and using the error: We investigate what are the properties of the error and how they correlate with the top-N recommendation quality, in methods that treat the missing entries as zeros. Then, we utilize the learned insights to develop a method, which explicitly uses the error. We have applied our algorithms to big datasets, with millions of ratings, that span different areas, such as grocery transactions, movie ratings, and retail transactions, showing significant improvements over the state-of-the-art.
Subjects/Keywords: Recommender Systems; Top-N Recommendation
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APA (6th Edition):
Christakopoulou, E. (2018). Improving the Quality of Top-N Recommendation. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/195398
Chicago Manual of Style (16th Edition):
Christakopoulou, Evangelia. “Improving the Quality of Top-N Recommendation.” 2018. Doctoral Dissertation, University of Minnesota. Accessed March 06, 2021.
http://hdl.handle.net/11299/195398.
MLA Handbook (7th Edition):
Christakopoulou, Evangelia. “Improving the Quality of Top-N Recommendation.” 2018. Web. 06 Mar 2021.
Vancouver:
Christakopoulou E. Improving the Quality of Top-N Recommendation. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/11299/195398.
Council of Science Editors:
Christakopoulou E. Improving the Quality of Top-N Recommendation. [Doctoral Dissertation]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/195398
Brock University
2.
Daneshmandmehrabani, Mahsa.
Towards a New Algorithm for Event Recommendation System
.
Degree: Department of Mathematics, Brock University
URL: http://hdl.handle.net/10464/12897
► We develop a recommendation algorithm for a local entertainment and ticket provider company. The recommender system predicts the score of items, i.e. event, for each…
(more)
▼ We develop a recommendation algorithm for a local entertainment and ticket provider company. The recommender system predicts the score of items, i.e. event, for each user. The special feature of these events, which makes them very different from similar settings, is that they are perishable: each event has a relatively short and specific lifespan. Therefore there is no explicit feedback available for a future event. Moreover, there is a very short description provided for each event and thus the keywords play a more than usual important role in categorizing each event. We provide a hybrid algorithm that utilizes content-based and collaborative filtering recommendations. We also present an axiomatic analysis of our model. These axioms are mostly derived from social choice theory.
Subjects/Keywords: Recommendation Systems
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APA (6th Edition):
Daneshmandmehrabani, M. (n.d.). Towards a New Algorithm for Event Recommendation System
. (Thesis). Brock University. Retrieved from http://hdl.handle.net/10464/12897
Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Daneshmandmehrabani, Mahsa. “Towards a New Algorithm for Event Recommendation System
.” Thesis, Brock University. Accessed March 06, 2021.
http://hdl.handle.net/10464/12897.
Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Daneshmandmehrabani, Mahsa. “Towards a New Algorithm for Event Recommendation System
.” Web. 06 Mar 2021.
Note: this citation may be lacking information needed for this citation format:
No year of publication.
Vancouver:
Daneshmandmehrabani M. Towards a New Algorithm for Event Recommendation System
. [Internet] [Thesis]. Brock University; [cited 2021 Mar 06].
Available from: http://hdl.handle.net/10464/12897.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.
Council of Science Editors:
Daneshmandmehrabani M. Towards a New Algorithm for Event Recommendation System
. [Thesis]. Brock University; Available from: http://hdl.handle.net/10464/12897
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.
King Abdullah University of Science and Technology
3.
Altaf, Basmah.
A Study of Fairness and Information Heterogeneity in Recommendation Systems.
Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2019, King Abdullah University of Science and Technology
URL: http://hdl.handle.net/10754/660257
► Recommender systems are an integral and successful application of machine learning in e-commerce industry and in everyday lives of online users. Recommendation algorithms are used…
(more)
▼ Recommender
systems are an integral and successful application of machine learning
in e-commerce industry and in everyday lives of online users.
Recommendation
algorithms are used extensively for news, musics, books, point of interests, or travel
recommendation as well as in many other domains. Although much focus has been
paid on improving
recommendation quality, however, some real-world aspects are not
considered: How to ensure that top-n recommendations are fair and not biased due to any popularity boosting events, such as awards for movies or songs? How to recommend items to entities by explicitly considering information from heterogeneous
sources. What is the best way to model sequential
recommendation systems as heterogeneous context-aware design, and learning on-the- y from spatial, temporal and social contexts. Can we model attributes and heterogeneous relations in a heterogeneous information network?
The goal of this thesis is to pave the way towards the next generation of realworld
recommendation systems tackling fairness and information heterogeneity challenges
to improve the user experience, while giving good recommendations. This thesis
bridges techniques from
recommendation and deep-learning techniques for representation learning by proposing novel techniques to address the above real-world problems.
We focus on four directions: (1) model the e ect of popularity bias over time on the
consumption of items, (2) model the heterogeneous information associated with sequential history of users and social links for sequential
recommendation, (3) model the heterogeneous links and rich content of nodes in an academic heterogeneous information network, and (4) learn semantics using topic modeling for nodes based on their content and heterogeneous links in a heterogeneous information network
Advisors/Committee Members: Zhang, Xiangliang (advisor), Moshkov, Mikhail (committee member), Shihada, Basem (committee member), Yan, Rui (committee member).
Subjects/Keywords: Recommendation Systems (RS); Fairness; Information Heterogeneity; Dataset Recommendation; Sequential Recommendation; Movie Award Bias
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APA (6th Edition):
Altaf, B. (2019). A Study of Fairness and Information Heterogeneity in Recommendation Systems. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/660257
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Altaf, Basmah. “A Study of Fairness and Information Heterogeneity in Recommendation Systems.” 2019. Thesis, King Abdullah University of Science and Technology. Accessed March 06, 2021.
http://hdl.handle.net/10754/660257.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Altaf, Basmah. “A Study of Fairness and Information Heterogeneity in Recommendation Systems.” 2019. Web. 06 Mar 2021.
Vancouver:
Altaf B. A Study of Fairness and Information Heterogeneity in Recommendation Systems. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2019. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/10754/660257.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Altaf B. A Study of Fairness and Information Heterogeneity in Recommendation Systems. [Thesis]. King Abdullah University of Science and Technology; 2019. Available from: http://hdl.handle.net/10754/660257
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Minnesota
4.
Kwon, YoungOk.
Computational techniques for more accurate and diverse recommendations.
Degree: PhD, Business Administration, 2011, University of Minnesota
URL: http://purl.umn.edu/115930
► Recommender systems are becoming an increasingly important research area due to the growing demand for personalized recommendations. The volume of information available to each user…
(more)
▼ Recommender systems are becoming an increasingly important research area due to the growing demand for personalized recommendations. The volume of information available to each user and the number of products carried in e-commerce marketplaces have grown tremendously. Thus, recommender systems are needed to help individual users find the most relevant items from an enormous number of choices and eventually increase sales by exposing users to what they may like, but may not have considered otherwise. Despite significant progress in developing new recommendation techniques within both industry and academia, most research, to date, has focused on improving recommendation accuracy (i.e., the accuracy with which the recommender system predicts users` ratings for items they have not yet rated). While recommendation accuracy is undoubtedly important, there is a growing understanding that accuracy does not always imply usefulness to users. Therefore, in addition to investigating the accuracy of recommendations, my dissertation also considers the diversity of recommendations as another important aspect of recommendation quality and explores the relationship between accuracy and diversity. The diversity of recommendations can be expressed by the number of unique items recommended across all users, which reflects the ability of recommender systems to go beyond the obvious, best-selling items, and to generate more idiosyncratic, personalized, and long-tail recommendations.
This dissertation presents four studies which propose new recommendation approaches that can improve accuracy and diversity. The first study enhances traditional recommendation algorithms by augmenting them with multi-criteria rating information for more accurate recommendations. The second study applies heuristic-based ranking approaches for more diverse recommendations. The third study develops more sophisticated optimization approaches for direct diversity maximization. The fourth study explores the possible combinations of the two types of approaches - incorporation of multi-criteria rating information and the use of different ranking methods - as a way to generate recommendations that are both more accurate and more diverse.
The new recommendation approaches proposed in this dissertation enrich the body of knowledge on recommender systems by extending single-rating recommendation problems to address multi-criteria recommendation problems and exploring new ways to tackle the accuracy-diversity tradeoff issue. Individual users and online content providers will also benefit from the proposed approaches, in that each user will find more relevant and personalized items from more accurate and diverse recommendations provided by recommender systems. These approaches could potentially lead to increased loyalty and sales, thus, benefiting the providers as well.
Subjects/Keywords: Recommendation Accuracy; Recommendation Diversity; Recommender Systems; Business Administration
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APA (6th Edition):
Kwon, Y. (2011). Computational techniques for more accurate and diverse recommendations. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/115930
Chicago Manual of Style (16th Edition):
Kwon, YoungOk. “Computational techniques for more accurate and diverse recommendations.” 2011. Doctoral Dissertation, University of Minnesota. Accessed March 06, 2021.
http://purl.umn.edu/115930.
MLA Handbook (7th Edition):
Kwon, YoungOk. “Computational techniques for more accurate and diverse recommendations.” 2011. Web. 06 Mar 2021.
Vancouver:
Kwon Y. Computational techniques for more accurate and diverse recommendations. [Internet] [Doctoral dissertation]. University of Minnesota; 2011. [cited 2021 Mar 06].
Available from: http://purl.umn.edu/115930.
Council of Science Editors:
Kwon Y. Computational techniques for more accurate and diverse recommendations. [Doctoral Dissertation]. University of Minnesota; 2011. Available from: http://purl.umn.edu/115930
Temple University
5.
Zhao, Feipeng.
Learning Top-N Recommender Systems with Implicit Feedbacks.
Degree: PhD, 2017, Temple University
URL: http://digital.library.temple.edu/u?/p245801coll10,450099
► Computer and Information Science
Top-N recommender systems automatically recommend N items for users from huge amounts of products. Personalized Top-N recommender systems have great impact…
(more)
▼ Computer and Information Science
Top-N recommender systems automatically recommend N items for users from huge amounts of products. Personalized Top-N recommender systems have great impact on many real world applications such as E-commerce platforms and social networks. Sometimes there is no rating information in user-item feedback matrix but only implicit purchase or browsing history, that means the user-item feedback matrix is a binary matrix, we call such feedbacks as implicit feedbacks. In our work we try to learn Top-N recommender systems with implicit feedbacks. First, we design a heterogeneous loss function to learn the model. Second, we incorporate item side information into recommender systems. We formulate a low-rank constraint minimization problem and give a closed-form solution for it. Third, we also use item side information to learn recommender systems. We use gradient descent method to learn our model. Most existing methods produce personalized top-N recommendations by minimizing a specific uniform loss such as pairwise ranking loss or pointwise recovery loss. In our first model, we propose a novel personalized Top-N recommendation approach that minimizes a combined heterogeneous loss based on linear self-recovery models. The heterogeneous loss integrates the strengths of both pairwise ranking loss and pointwise recovery loss to provide more informative recommendation predictions. We formulate the learning problem with heterogeneous loss as a constrained convex minimization problem and develop a projected stochastic gradient descent optimization algorithm to solve it. Most previous systems are only based on the user-item feedback matrix. In many applications, in addition to the user-item rating/purchase matrix, item-based side information such as product reviews, book reviews, item comments, and movie plots can be easily collected from the Internet. This abundant item-based information can be used for recommendation systems. In the second model, we propose a novel predictive collaborative filtering approach that exploits both the partially observed user-item recommendation matrix and the item-based side information to produce top-N recommender systems. The proposed approach automatically identifies the most interesting items for each user from his or her non-recommended item pool by aggregating over his or her recommended items via a low-rank coefficient matrix. Moreover, it also simultaneously builds linear regression models from the item-based side information such as item reviews to predict the item recommendation scores for the users. The proposed approach is formulated as a rank constrained joint minimization problem with integrated least squares losses, for which an efficient analytical solution can be derived. In the third model, we also propose a joint discriminative prediction model that exploits both the partially observed user-item recommendation matrix and the item-based side information to build top-N recommender systems. This joint model aggregates observed user-item recommendation…
Advisors/Committee Members: Guo, Yuhong;, Shi, Justing Y., Dragut, Eduard Constantin, Dong, Yuexiao;.
Subjects/Keywords: Computer science;
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APA ·
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MLA ·
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APA (6th Edition):
Zhao, F. (2017). Learning Top-N Recommender Systems with Implicit Feedbacks. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,450099
Chicago Manual of Style (16th Edition):
Zhao, Feipeng. “Learning Top-N Recommender Systems with Implicit Feedbacks.” 2017. Doctoral Dissertation, Temple University. Accessed March 06, 2021.
http://digital.library.temple.edu/u?/p245801coll10,450099.
MLA Handbook (7th Edition):
Zhao, Feipeng. “Learning Top-N Recommender Systems with Implicit Feedbacks.” 2017. Web. 06 Mar 2021.
Vancouver:
Zhao F. Learning Top-N Recommender Systems with Implicit Feedbacks. [Internet] [Doctoral dissertation]. Temple University; 2017. [cited 2021 Mar 06].
Available from: http://digital.library.temple.edu/u?/p245801coll10,450099.
Council of Science Editors:
Zhao F. Learning Top-N Recommender Systems with Implicit Feedbacks. [Doctoral Dissertation]. Temple University; 2017. Available from: http://digital.library.temple.edu/u?/p245801coll10,450099
Tampere University
6.
Machado, Lucas.
Fair team recommendations for multidisciplinary projects
.
Degree: 2019, Tampere University
URL: https://trepo.tuni.fi/handle/10024/105736
► With the ever increasing amount of data in the world, it becomes harder to find useful and desired information. Recommender systems, which offer a way…
(more)
▼ With the ever increasing amount of data in the world, it becomes harder to find useful and desired information. Recommender systems, which offer a way to analyze that data and suggest relevant information, are already common nowadays and a important part of several systems and services. While recommender systems are often used for suggesting items for users, there are not many studies about using them for problems such as team formation. This thesis focus on exploring a variation of that problem, in which teams have multidisciplinary requirements and members' selection is based on the match of their skills and the requirements. In addition, when assembling multiple teams there is a challenge of allocating the best members in a fair way between the teams.
With the studied concepts from the literature, this thesis suggests a brute force and a faster heuristic method as solutions to create team recommendations to multidisciplinary projects. Furthermore, to increase the fairness between the recommended teams, the K-rounds and Pairs-rounds methods are proposed as variations of the heuristic approach.
Several different test scenarios are executed to analyze and compare the efficiency and efficacy of these methods, and it is found that the heuristic-based methods are able to provide the same levels of quality with immensely greater performance than the brute force approach. The K-rounds method is able to generate substantially more fair team recommendations, while keeping the same levels of quality and performance as other methods. The Pairs-rounds method presents slightly better recommendations quality-wise than the K-rounds method, but its recommendations are less fair to a small degree. The proposed methods perform well enough for use in real scenarios.
Subjects/Keywords: recommender systems;
fairness;
group formation;
team recommendation
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APA (6th Edition):
Machado, L. (2019). Fair team recommendations for multidisciplinary projects
. (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/105736
Chicago Manual of Style (16th Edition):
Machado, Lucas. “Fair team recommendations for multidisciplinary projects
.” 2019. Masters Thesis, Tampere University. Accessed March 06, 2021.
https://trepo.tuni.fi/handle/10024/105736.
MLA Handbook (7th Edition):
Machado, Lucas. “Fair team recommendations for multidisciplinary projects
.” 2019. Web. 06 Mar 2021.
Vancouver:
Machado L. Fair team recommendations for multidisciplinary projects
. [Internet] [Masters thesis]. Tampere University; 2019. [cited 2021 Mar 06].
Available from: https://trepo.tuni.fi/handle/10024/105736.
Council of Science Editors:
Machado L. Fair team recommendations for multidisciplinary projects
. [Masters Thesis]. Tampere University; 2019. Available from: https://trepo.tuni.fi/handle/10024/105736
Iowa State University
7.
Wang, Yen-yao.
Antecedents of Review and Recommendation Systems Acceptance.
Degree: 2011, Iowa State University
URL: https://lib.dr.iastate.edu/etd/11203
► Online recommendation systems, which are becoming increasingly prevalent on the Web, help reduce information overload, support quality purchasing decisions, and increase consumer confidence in the…
(more)
▼ Online recommendation systems, which are becoming increasingly prevalent on the Web, help reduce information overload, support quality purchasing decisions, and increase consumer confidence in the products they buy. Researchers of recommendation systems have focused more on how to provide a better recommendation system in terms of algorithm and mechanism. However, research which has empirically documented the link between customers' motivations and intentions to use recommendation systems is scant. Therefore, the aim of this study attempts to explore how consumers assess the quality of two types of recommendation systems, collaborative filtering and content-based by using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model. Specifically, the under-investigated concept of trust in technological artifacts is adapted to the UTAUT model.
In addition, this study considers hedonic and utilitarian product characteristics, attempting to present a comprehensive range of recommendation systems. A total of 51 participants completed an online 2 (recommendation systems) x 2 (products) survey. The quantitative analysis of the questionnaires was conducted through multiple regression and path analysis in order to determine relationships across various dimensions.
Results of this study showed that types of recommendation systems and products did have different effects on behavioral intention to use recommendation systems. To conclude, this study may be of importance in explaining factors contributing to use recommendation systems, as well as in providing designers of recommendation systems with a better understanding of how to provide a more effective recommendation system.
In addition, this study considers hedonic and utilitarian product characteristics, attempting to present a comprehensive range of recommendation systems. A total of 51 participants completed an online 2 (recommendation systems) x 2 (products) survey. The quantitative analysis of the questionnaires was conducted through multiple regression and path analysis in order to determine relationships across various dimensions.
Results of this study showed that types of recommendation systems and products did have different effects on behavioral intention to use recommendation systems. To conclude, this study may be of importance in explaining factors contributing to use recommendation systems, as well as in providing designers of recommendation systems with a better understanding of how to provide a more effective recommendation system.
Subjects/Keywords: Hedonic; Recommendation systems; Trust; UTAUT; Utilitarian; Business
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APA ·
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Manager
APA (6th Edition):
Wang, Y. (2011). Antecedents of Review and Recommendation Systems Acceptance. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/11203
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Wang, Yen-yao. “Antecedents of Review and Recommendation Systems Acceptance.” 2011. Thesis, Iowa State University. Accessed March 06, 2021.
https://lib.dr.iastate.edu/etd/11203.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wang, Yen-yao. “Antecedents of Review and Recommendation Systems Acceptance.” 2011. Web. 06 Mar 2021.
Vancouver:
Wang Y. Antecedents of Review and Recommendation Systems Acceptance. [Internet] [Thesis]. Iowa State University; 2011. [cited 2021 Mar 06].
Available from: https://lib.dr.iastate.edu/etd/11203.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wang Y. Antecedents of Review and Recommendation Systems Acceptance. [Thesis]. Iowa State University; 2011. Available from: https://lib.dr.iastate.edu/etd/11203
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
8.
Chandrasekaran Ayyanathan, P.S.N. (author).
Where will you comment next? Exploiting comments for personalized recommendations.
Degree: 2015, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:dba67190-9f2f-479a-9409-20fdcf7e69ee
► Since the advent of Web 2.0, users have not only increasingly created content, but also contributed reactions to content in the form of comments. Comments…
(more)
▼ Since the advent of Web 2.0, users have not only increasingly created content, but also contributed reactions to content in the form of comments. Comments are challenging to analyze due to their short lengths and informal style, meaning that any individual comment provides very little data to work with and is highly variable. However, comments capture innate and an explicit opinion of a user that makes it invaluable towards personalization. In this work, we explore the possibilities of exploiting comments towards the end of personalized recommendations. Over the course of this work, we investigate the particular domain of news recommendation and report our findings through use of different recommenders evaluated offline. Our contributions include an evaluation strategy that allows for simulation of recommenders offline, a simplistic hybrid filtering technique that exploits the advantages of its root recommenders and various findings related to news recommendation in general. We perform a preliminary study into investigating whether users maybe attributed by comments they make and find that they are indeed attributable if the right features are considered. Utilizing the property of authorship attribution through comments, we achieve user-user similarity that ultimately aids in delivering recommendations. We find that freshness is an important aspect in news recommendation and therefore for the design of our recommender we build upon the freshness aspect while also achieving personalization by exploiting content, user-user similarity and the user neighbourhood.
Multimedia Computing
Intelligent Systems
Electrical Engineering, Mathematics and Computer Science
Advisors/Committee Members: Larson, M.A. (mentor).
Subjects/Keywords: recommendation systems; information retrieval; machine learning
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APA (6th Edition):
Chandrasekaran Ayyanathan, P. S. N. (. (2015). Where will you comment next? Exploiting comments for personalized recommendations. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:dba67190-9f2f-479a-9409-20fdcf7e69ee
Chicago Manual of Style (16th Edition):
Chandrasekaran Ayyanathan, P S N (author). “Where will you comment next? Exploiting comments for personalized recommendations.” 2015. Masters Thesis, Delft University of Technology. Accessed March 06, 2021.
http://resolver.tudelft.nl/uuid:dba67190-9f2f-479a-9409-20fdcf7e69ee.
MLA Handbook (7th Edition):
Chandrasekaran Ayyanathan, P S N (author). “Where will you comment next? Exploiting comments for personalized recommendations.” 2015. Web. 06 Mar 2021.
Vancouver:
Chandrasekaran Ayyanathan PSN(. Where will you comment next? Exploiting comments for personalized recommendations. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2021 Mar 06].
Available from: http://resolver.tudelft.nl/uuid:dba67190-9f2f-479a-9409-20fdcf7e69ee.
Council of Science Editors:
Chandrasekaran Ayyanathan PSN(. Where will you comment next? Exploiting comments for personalized recommendations. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:dba67190-9f2f-479a-9409-20fdcf7e69ee
University of Illinois – Urbana-Champaign
9.
Norick, Brandon.
Leveraging heterogeneous information networks for personalized entity recommendation.
Degree: MS, Computer Science, 2017, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/99227
► Recommendation is a challenging but important task which has applications in nearly every sector of industry as well as in academia. There are a wide…
(more)
▼ Recommendation is a challenging but important task which has applications in nearly every sector of industry as well as in academia. There are a wide variety of approaches to the
recommendation problem, with network-based techniques garnering increasing interest and study in recent years. However, most of these studies only explore the problem in the context of a single relationship between entities, such as a following relationship in a social network like Twitter. Such approaches ignore the complex environment in which most
recommendation tasks exist in favor of simplifying the problem. The complexity of human decision making necessitates approaches which can utilize the heterogeneous environments in which the
recommendation task is set rather than reducing them to single relationship.
In this work, we explore the problem of entity
recommendation without such a simplification, instead we utilize heterogeneous information networks to capture the complexity of the behaviors for which we are seeking to make recommendations. Our proposed approach captures the different behaviors of individuals by examining their heterogeneous relationships in the network and as a result can provide high-quality personalized recommendations from implicit feedback represented in heterogeneous information networks.
We begin by introducing meta-path-based latent features, which capture the connectivity of entities in the network along different paths, giving us a foundation which explicitly accounts for the heterogeneous nature of the task. Upon this foundation we build a global
recommendation model using a ranking optimization technique known as Bayesian Personalized Ranking. We extend this global model into a personalized model, building a model which can capture the differences present in the network that describe the preferences of different users. Finally, empirical studies show that our techniques are more effective than several popular and state-of-the-art entity recommendations techniques.
Advisors/Committee Members: Han, Jiawei (advisor).
Subjects/Keywords: Recommender systems; Entity recommendation; Heterogeneous information networks
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APA (6th Edition):
Norick, B. (2017). Leveraging heterogeneous information networks for personalized entity recommendation. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99227
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Norick, Brandon. “Leveraging heterogeneous information networks for personalized entity recommendation.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed March 06, 2021.
http://hdl.handle.net/2142/99227.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Norick, Brandon. “Leveraging heterogeneous information networks for personalized entity recommendation.” 2017. Web. 06 Mar 2021.
Vancouver:
Norick B. Leveraging heterogeneous information networks for personalized entity recommendation. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/2142/99227.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Norick B. Leveraging heterogeneous information networks for personalized entity recommendation. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99227
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
California State University – Northridge
10.
Klotzman, Vanessa.
The Intelligent Degree Planner.
Degree: MS, Computer Science, 2020, California State University – Northridge
URL: http://hdl.handle.net/10211.3/214749
► There exists a science in developing personality and aptitude tests to determine what career specialty works for a certain individual. The Intelligent Degree Planner will…
(more)
▼ There exists a science in developing personality and aptitude tests to determine what career specialty works for a certain individual. The Intelligent Degree Planner will explore this science differently, but with the idea of Natural Language Processing. As with using human language we can use unique classification algorithms to categorize and match what to study, general education courses to take, and major electives to take. Many factors are taken into consideration when an incoming college student picks a course of study from their abilities, socioeconomic status, race, and etc. The purpose of the Intelligent Degree Planner algorithms is to determine if we can create a tool to serve as guidelines for students, thereby paving the way for a smooth path to graduation rate. The Intelligent Degree Planner will try to attempt using natural language processing by trying to understand the student's wants and desires. Additionally, The Intelligent Degree Planner will function with the help of the curriculum web service developed by META+LAB which will retrieve CSUN major/minor catalog. On top of recommendations of what to study in university, we will determine if we can fetch graduate schools, and companies that hire based on field of study. We want to determine if we can create a software to help in having a smooth path toward graduation. The Intelligent Degree Planner is a proof of concept. We are using it to serve as a basis of how machine learning can help with ensuring students have the best outcome. Due to the limitations of data collections, we will assume students have no barriers and stopping is stopping them. This study will explore a variety of different text classification techniques from using a multinomial Naive Bayes classifier to a LSTM. We will then test the model with our student data. The text classification model is trying to attempt that based on the student's words, classify them into different majors based upon the content of the strings. If the resulted major has less than 120 units, then the second most likely major will be considered a minor. If adding a minor does not fulfill the 120 unit major requirement, an additional minor will be recommended. As the university, allows a max of two minors. Then we will determine if we can generate different categories of general education courses one should take using a similar approach. If general education recommendations are not generated for one category, we will explore how we can fix this scenario in the future. Development and investigation is not quite yet done. After general education courses are picked, we will use the data about what major was picked in conjunction with an RSSFeed to find careers associated with that major. Additionally, we will determine how we can match graduate schools with the appropriate major. Finally, we will determine if this most favorable way to generate a "defined path" , as more data will slowly be collected over time. We will look into what metrics can be used on these algorithms. To conclude, we will determine if a…
Advisors/Committee Members: Wiegley, Jeffrey (advisor), Fitzgerald, Steven (committee member).
Subjects/Keywords: Recommendation Systems; Dissertations, Academic – CSUN – Computer Science.
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APA ·
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Export
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APA (6th Edition):
Klotzman, V. (2020). The Intelligent Degree Planner. (Masters Thesis). California State University – Northridge. Retrieved from http://hdl.handle.net/10211.3/214749
Chicago Manual of Style (16th Edition):
Klotzman, Vanessa. “The Intelligent Degree Planner.” 2020. Masters Thesis, California State University – Northridge. Accessed March 06, 2021.
http://hdl.handle.net/10211.3/214749.
MLA Handbook (7th Edition):
Klotzman, Vanessa. “The Intelligent Degree Planner.” 2020. Web. 06 Mar 2021.
Vancouver:
Klotzman V. The Intelligent Degree Planner. [Internet] [Masters thesis]. California State University – Northridge; 2020. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/10211.3/214749.
Council of Science Editors:
Klotzman V. The Intelligent Degree Planner. [Masters Thesis]. California State University – Northridge; 2020. Available from: http://hdl.handle.net/10211.3/214749
Rutgers University
11.
Ge, Yingqiang, 1993-.
Maximizing marginal utility per dollar for economic recommendation.
Degree: MS, Computer Science, 2019, Rutgers University
URL: https://rucore.libraries.rutgers.edu/rutgers-lib/61748/
► Understanding the economic nature of consumer decisions in e-Commerce is important to personalized recommendation systems. Established economic theories claim that informed consumers always attempt to…
(more)
▼ Understanding the economic nature of consumer decisions in e-Commerce is important to personalized
recommendation systems. Established economic theories claim that informed consumers always attempt to maximize their utility by choosing the items of the largest marginal utility per dollar (MUD) within their budget. For example, gaining 5 dollars of extra benefit by spending 10 dollars makes a consumer much more satisfied than having the same amount of extra benefit by spending 20 dollars, although the second product may have a higher absolute utility value. Meanwhile, making purchases online may be risky decisions that could cause dissatisfaction. For example, people may give low ratings towards purchased items that they thought they would like when placing the order. Therefore, the design of recommender
systems should also take users' risk attitudes into consideration to better learn consumer behaviors.
Motivated by the first consideration, in this paper, we propose a learning algorithm to maximize marginal utility per dollar for
recommendation. With the second, economic theory shows that rational people can be arbitrarily close to risk neutral when stakes are arbitrarily small, and this is generally applicable to consumer online purchase behaviors because most people spend a small portion of their total wealth for a single purchase. To integrate this theory with machine learning, we propose to augment MUD optimization with approximate risk-neural constraint to generate personalized recommendations. Experiments on real-world e-Commerce datasets show that our approach is able to achieve better performance than many classical
recommendation methods, in terms of both traditional
recommendation measures such as precision and recall, as well as economic measures such as MUD.
Advisors/Committee Members: Zhang, Yongfeng (chair), School of Graduate Studies.
Subjects/Keywords: Recommendation systems; Marginal utility; Electronic commerce
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APA (6th Edition):
Ge, Yingqiang, 1. (2019). Maximizing marginal utility per dollar for economic recommendation. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/61748/
Chicago Manual of Style (16th Edition):
Ge, Yingqiang, 1993-. “Maximizing marginal utility per dollar for economic recommendation.” 2019. Masters Thesis, Rutgers University. Accessed March 06, 2021.
https://rucore.libraries.rutgers.edu/rutgers-lib/61748/.
MLA Handbook (7th Edition):
Ge, Yingqiang, 1993-. “Maximizing marginal utility per dollar for economic recommendation.” 2019. Web. 06 Mar 2021.
Vancouver:
Ge, Yingqiang 1. Maximizing marginal utility per dollar for economic recommendation. [Internet] [Masters thesis]. Rutgers University; 2019. [cited 2021 Mar 06].
Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61748/.
Council of Science Editors:
Ge, Yingqiang 1. Maximizing marginal utility per dollar for economic recommendation. [Masters Thesis]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61748/
University of Minnesota
12.
Christakopoulou, Konstantina.
Towards Recommendation Systems with Real-World Constraints.
Degree: PhD, Computer Science, 2018, University of Minnesota
URL: http://hdl.handle.net/11299/201062
► Recommendation systems have become an integral part of our everyday lives. Although there have been many works focusing on recommendation quality, many real-world aspects of…
(more)
▼ Recommendation systems have become an integral part of our everyday lives. Although there have been many works focusing on recommendation quality, many real-world aspects of the recommendation process are typically overlooked: How can we ensure that the very top recommendations users see are engaging? How to recommend venues matching user interests, while preventing many users from being directed to the same venue? Can we design recommenders which first converse with users and then give a recommendation? What is the best way to model recommendation systems as interactive systems, while learning on-the-fly the user-item structure? To what extent can a malicious party perform machine learned adversarial attacks against a recommender? The goal of this thesis is to pave the way towards the next generation of recommendation systems tackling such real-world challenges to improve the user experience, while giving good recommendations. This thesis, bridging techniques from machine learning, optimization, and real-world insights, introduces novel tools to address the above questions. We focus on three directions: (1) encoding real-world constraints into the objective functions, (2) learning to interact with users, and (3) modeling machine learned fake users with malicious goals. For the first direction, by adjusting the optimization objective to capture real-world constraints – (1a) the screen space is small, creating the need for the top recommendations to be relevant, (1b) the item capacities are limited – we suitably guide the learning of model parameters. For the second direction, to balance the need to explore users' preferences with the desire to exploit what has been learned, at a large user and item scale, we combine interactive learning techniques with the principle that similar users tend to behave similarly. This combination results in novel recommendation systems that learn to (2a) converse with new users, and (2b) collaboratively interact with users. For the third direction, taking the perspective of an adversary of the recommender, we use machine learning to learn fake user profiles, which are indistinguishable from real ones, while having a malicious goal. Illustrating the vulnerability of modern recommenders to machine learned attacks will arguably create new directions for designing robust recommendation systems against such attacks.
Subjects/Keywords: adversarial examples; conversational recommendation; interactive learning; machine learning; real-world constraints; recommendation systems
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APA (6th Edition):
Christakopoulou, K. (2018). Towards Recommendation Systems with Real-World Constraints. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/201062
Chicago Manual of Style (16th Edition):
Christakopoulou, Konstantina. “Towards Recommendation Systems with Real-World Constraints.” 2018. Doctoral Dissertation, University of Minnesota. Accessed March 06, 2021.
http://hdl.handle.net/11299/201062.
MLA Handbook (7th Edition):
Christakopoulou, Konstantina. “Towards Recommendation Systems with Real-World Constraints.” 2018. Web. 06 Mar 2021.
Vancouver:
Christakopoulou K. Towards Recommendation Systems with Real-World Constraints. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/11299/201062.
Council of Science Editors:
Christakopoulou K. Towards Recommendation Systems with Real-World Constraints. [Doctoral Dissertation]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/201062
13.
Al-Ghossein, Marie.
Context-aware recommender systems for real-world applications : Systèmes de recommandation contextuels pour les applications du monde réel.
Degree: Docteur es, Informatique, 2019, Université Paris-Saclay (ComUE)
URL: http://www.theses.fr/2019SACLT008
► Les systèmes de recommandation se sont révélés être des outils efficaces pour aider les utilisateurs à faire face à la surcharge informationnelle. D’importants progrès ont…
(more)
▼ Les systèmes de recommandation se sont révélés être des outils efficaces pour aider les utilisateurs à faire face à la surcharge informationnelle. D’importants progrès ont été réalisés dans le domaine durant les deux dernières décennies, menant en particulier à l’exploitation de l’information contextuelle pour modéliser l’aspect dynamique des utilisateurs et des articles. La définition traditionnelle du contexte, adoptée dans la plupart des systèmes de recommandation contextuels, ne répond pas à plusieurs contraintes rencontrées dans les applications du monde réel. Dans cette thèse, nous abordons les problèmes de recommandation en présence d’informations contextuelles partiellement observables et d’informations contextuelles non observables dans deux applications particulières, la recommandation d’hôtels et la recommandation en ligne, remettant en question plusieurs aspects de la définition traditionnelle du contexte, notamment l'accessibilité, la pertinence, l'acquisition et la modélisation.La première partie de la thèse étudie le problème de recommandation d’hôtels qui souffre du démarrage à froid continu, limitant la performance des approches classiques de recommandation. Le voyage n’est pas une activité fréquente et les utilisateurs ont tendance à adopter des comportements diversifiés en fonction de leurs situations spécifiques. Après une analyse du comportement des utilisateurs dans ce domaine, nous proposons de nouvelles approches de recommandation intégrant des informations contextuelles partiellement observables affectant les utilisateurs. Nous montrons comment cela contribue à améliorer la qualité des recommandations.La deuxième partie de la thèse aborde le problème de recommandation en ligne en présence de flux de données où les observations apparaissent continûment à haute fréquence. Nous considérons que les utilisateurs et les articles reposent sur des informations contextuelles non observables par le système et évoluent de façons différentes à des rythmes différents. Nous proposons alors d’effectuer de la détection active de changements et d’assurer la mise à jour des modèles en temps réel. Nous concevons de nouvelles méthodes qui s’adaptent aux changements qui apparaissent au niveau des préférences des utilisateurs et des perceptions et descriptions des articles, et montrons l’importance de la recommandation adaptative en ligne pour garantir de bonnes performances au cours du temps.
Recommender systems have proven to be valuable tools to help users overcome the information overload, and significant advances have been made in the field over the last two decades. In particular, contextual information has been leveraged to model the dynamics occurring within users and items. Context is a complex notion and its traditional definition, which is adopted in most recommender systems, fails to cope with several issues occurring in real-world applications. In this thesis, we address the problems of partially observable and unobservable contexts in two particular applications, hotel recommendation and online…
Advisors/Committee Members: Abdessalem, Talel (thesis director).
Subjects/Keywords: Systèmes de recommandation; Recommandation d'hôtels; Recommandation en ligne; Recommender systems; Hotel recommendation; Online recommendation
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APA ·
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MLA ·
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Export
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Manager
APA (6th Edition):
Al-Ghossein, M. (2019). Context-aware recommender systems for real-world applications : Systèmes de recommandation contextuels pour les applications du monde réel. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2019SACLT008
Chicago Manual of Style (16th Edition):
Al-Ghossein, Marie. “Context-aware recommender systems for real-world applications : Systèmes de recommandation contextuels pour les applications du monde réel.” 2019. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed March 06, 2021.
http://www.theses.fr/2019SACLT008.
MLA Handbook (7th Edition):
Al-Ghossein, Marie. “Context-aware recommender systems for real-world applications : Systèmes de recommandation contextuels pour les applications du monde réel.” 2019. Web. 06 Mar 2021.
Vancouver:
Al-Ghossein M. Context-aware recommender systems for real-world applications : Systèmes de recommandation contextuels pour les applications du monde réel. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2019. [cited 2021 Mar 06].
Available from: http://www.theses.fr/2019SACLT008.
Council of Science Editors:
Al-Ghossein M. Context-aware recommender systems for real-world applications : Systèmes de recommandation contextuels pour les applications du monde réel. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2019. Available from: http://www.theses.fr/2019SACLT008
14.
Canhoto, Vicente.
Recomendação de música: comparação entre collaborative filtering e context filtering.
Degree: 2013, RCAAP
URL: https://www.rcaap.pt/detail.jsp?id=oai:repositorio.iscte-iul.pt:10071/8062
► Mestrado em Gestão de Sistemas de Informação
A massificação de serviços de música online democratizou o acesso a milhões de músicas. No entanto, é impossível…
(more)
▼ Mestrado em Gestão de Sistemas de Informação
A massificação de serviços de música online democratizou o acesso a milhões de músicas. No entanto, é impossível para os utilizadores ouvirem e conhecerem todas essas músicas. De modo a auxiliar na sugestão sobre o que ouvir num dado momento, foram desenvolvidos sistemas que recomendam músicas ao utilizador. A técnica de Collaborative Filtering gera recomendações com base nas músicas ouvidas por utilizadores com gostos semelhantes. Apesar de apresentar um bom desempenho, vários investigadores propuseram melhoramentos aos mesmos. Um dos mais referidos é a utilização de informação contextual sobre o utilizador. A relação entre a utilização desta informação em sistemas de recomendação e o aumento da satisfação dos utilizadores foi provada por diversos investigadores.
O trabalho desenvolvido nesta dissertação focou-se na comparação entre um algoritmo de recomendação por Collaborative Filtering tradicional e outro baseado em determinados elementos do contexto. Para isso foi proposto e implementado um sistema de recomendação online que integra estas duas abordagens, apoiado numa revisão da literatura.
Por fim, este sistema foi utilizado numa experiência de campo online em que qualquer utilizador pôde fazer pedidos de recomendação. Estes pedidos foram servidos alternadamente por cada um dos algoritmos de recomendação, e foram registadas as avaliações dos utilizadores às músicas recomendadas de modo a aferir a sua satisfação com ambas as abordagens. Os resultados obtidos demonstram que a recomendação baseada no contexto foi superior ao Collaborative Filtering, exceção apenas para a fase inicial do funcionamento do sistema em que existiam poucos dados acerca das interações dos utilizadores com as músicas disponibilizadas
The massification of online music services democratized the access to millions of songs. Nevertheless, it is impossible for the users to enjoy and know all those songs. In order to assist in the suggestion about what to listen in a given moment, there have been developed systems which recommend music to the user. The well-known Collaborative Filtering technique generates recommendations based on the interests of users with similar tastes. Despite presenting a good performance, several investigators proposed improvements to it. One of the most mentioned is the use of contextual information about the user. The relationship between the use of this information in recommendation systems and increased user satisfaction has been proven by several investigators.
The work developed in this thesis was focused on the comparison between a Collaborative Filtering recommendation algorithm and a Context-based one. In order to achieve that, an online recommendation system that integrates these two approaches was proposed and implemented, supported by a literature review.
Finally, this system was used in an online study in which any user could make music recommendation requests. These requests were served alternately by each one of the implemented algorithms and the user’s…
Advisors/Committee Members: Serrão, Carlos, Cardoso, Elsa.
Subjects/Keywords: Sistema de recomendação; Recomendação de música; Collaborative filtering; Recomendação baseada no contexto; Context pre-filtering; Comparação de sistemas de recomendação; Recommendation system; Music recommendation; Context-based recommendation,; Recommendation systems comparison
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APA (6th Edition):
Canhoto, V. (2013). Recomendação de música: comparação entre collaborative filtering e context filtering. (Thesis). RCAAP. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:repositorio.iscte-iul.pt:10071/8062
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Canhoto, Vicente. “Recomendação de música: comparação entre collaborative filtering e context filtering.” 2013. Thesis, RCAAP. Accessed March 06, 2021.
https://www.rcaap.pt/detail.jsp?id=oai:repositorio.iscte-iul.pt:10071/8062.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Canhoto, Vicente. “Recomendação de música: comparação entre collaborative filtering e context filtering.” 2013. Web. 06 Mar 2021.
Vancouver:
Canhoto V. Recomendação de música: comparação entre collaborative filtering e context filtering. [Internet] [Thesis]. RCAAP; 2013. [cited 2021 Mar 06].
Available from: https://www.rcaap.pt/detail.jsp?id=oai:repositorio.iscte-iul.pt:10071/8062.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Canhoto V. Recomendação de música: comparação entre collaborative filtering e context filtering. [Thesis]. RCAAP; 2013. Available from: https://www.rcaap.pt/detail.jsp?id=oai:repositorio.iscte-iul.pt:10071/8062
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
NSYSU
15.
Chen, Lu-Wen.
A Neural Science Study on the Effect of Product Recommendation in Electronic Commerce.
Degree: Master, Information Management, 2016, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0803116-202527
► The rapid development of e-commerce, the problem facing consumers is not insufficient information but information overload. Therefore, recommendation systems have been used to reduce the…
(more)
▼ The rapid development of e-commerce, the problem facing consumers is not insufficient information but information overload. Therefore,
recommendation systems have been used to reduce the search cost and they have become an essential function for most websites to enhance consumerâs shopping experience.
Many previous studies have investigated the effect of product
recommendation with the goal of finding factors that influence consumerâs attitude and purchase intention. However, most of them used traditional questionnaires to measure collect subjective data, which may be biased and
subject to the common method bias. The purpose of this study is to collect objective physiological responses of the subjects using electroencephalogram (EEG) and eye-tracking techniques and compare the result with behavioral data collected from questionnaires.
We further used the neural science data to examine the elimination likelihood data for exploring possible mechanisms in the decision process. Our findings indicate that product type and consumerâs interest will affect the attitude toward the product in the behavioral study. Consumerâs attention levels vary in different product types and consumerâs interest levels based on the EEG and fixation times observed from the eye-tracker.
Advisors/Committee Members: Kai-Lung Hui (chair), Ting-Peng Liang (committee member), None (chair).
Subjects/Keywords: Brainwave; Electroencephalogram (EEG); Eyetracking; Recommendation systems; Neural information systems
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Export
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APA (6th Edition):
Chen, L. (2016). A Neural Science Study on the Effect of Product Recommendation in Electronic Commerce. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0803116-202527
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Chen, Lu-Wen. “A Neural Science Study on the Effect of Product Recommendation in Electronic Commerce.” 2016. Thesis, NSYSU. Accessed March 06, 2021.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0803116-202527.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Chen, Lu-Wen. “A Neural Science Study on the Effect of Product Recommendation in Electronic Commerce.” 2016. Web. 06 Mar 2021.
Vancouver:
Chen L. A Neural Science Study on the Effect of Product Recommendation in Electronic Commerce. [Internet] [Thesis]. NSYSU; 2016. [cited 2021 Mar 06].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0803116-202527.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Chen L. A Neural Science Study on the Effect of Product Recommendation in Electronic Commerce. [Thesis]. NSYSU; 2016. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0803116-202527
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of California – San Diego
16.
Verma, Chetan Kumar.
Enabling Automated and Efficient Personalization Systems.
Degree: Electrical Engineering (Computer Engineering), 2015, University of California – San Diego
URL: http://www.escholarship.org/uc/item/8tq2c9xn
► Information explosion and the increasing use of Internet have fueled the growing popularity of personalization systems. Such systems can understand user interests to customize the…
(more)
▼ Information explosion and the increasing use of Internet have fueled the growing popularity of personalization systems. Such systems can understand user interests to customize the information served to them, thereby addressing information overload. At the same time, personalization systems can also enable service and content providers to serve targeted advertisements and recommendations to users. In order to operate on the massive scales of the number of users and the amount of data available, personalization systems have a crucial requirement for automation and efficiency. In this dissertation, we identify key challenges faced by personalization systems and provide solutions to address them such that the above requirements can be achieved. We first note that content classification is an important component of personalization systems. Approaches to train classification models typically depend on manual collection and labeling of training data, which makes personalization non-scalable. To address this, we develop a completely automated framework that can provide labeled training data for arbitrary set of categories. Experiments using online videos demonstrate the feasibility and effectiveness of our approach. The second key challenge we address is the sparsity in annotations of popular online content such as Flickr images. Sparse or missing tags hamper the ability of personalization systems to recommend content or to infer the interests of users that access them. Towards this, we show how ontological tag trees can be constructed from corpus based statistics and semantic relationships between tags, to alleviate tag sparsity in a space efficient manner. Through evaluations, we demonstrate the effectiveness and efficiency of ontological tag trees as compared to existing methods. Lastly, we focus on alleviating information overload in enterprise repositories. We design a file metadata based recommendation system that captures per user access patterns and user collaboration to recommend new files. In order to address scalability concerns of per user modeling, we propose optimizations that significantly reduce the time to serve recommendations to users. Experiments over actual enterprise data show that more than two orders of speed up is obtained as a result of the proposed methods.
Subjects/Keywords: Computer science; automation; classification; efficiency; personalization systems; recommendation systems
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APA (6th Edition):
Verma, C. K. (2015). Enabling Automated and Efficient Personalization Systems. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/8tq2c9xn
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Verma, Chetan Kumar. “Enabling Automated and Efficient Personalization Systems.” 2015. Thesis, University of California – San Diego. Accessed March 06, 2021.
http://www.escholarship.org/uc/item/8tq2c9xn.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Verma, Chetan Kumar. “Enabling Automated and Efficient Personalization Systems.” 2015. Web. 06 Mar 2021.
Vancouver:
Verma CK. Enabling Automated and Efficient Personalization Systems. [Internet] [Thesis]. University of California – San Diego; 2015. [cited 2021 Mar 06].
Available from: http://www.escholarship.org/uc/item/8tq2c9xn.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Verma CK. Enabling Automated and Efficient Personalization Systems. [Thesis]. University of California – San Diego; 2015. Available from: http://www.escholarship.org/uc/item/8tq2c9xn
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
17.
Coulibaly, Adama.
Décision de groupe, Aide à la facilitation : ajustement de procédure de vote selon le contexte de décision : Group decision, Facilitation assistance : Adjustment of voting procedure according to the context of the decision.
Degree: Docteur es, Informatique et télécommunications, 2019, Université Toulouse I – Capitole
URL: http://www.theses.fr/2019TOU10011
► La facilitation est un élément central dans une prise de décision de groupe surtout en faisant l'usage des outils de nouvelle technologie. Le facilitateur, pour…
(more)
▼ La facilitation est un élément central dans une prise de décision de groupe surtout en faisant l'usage des outils de nouvelle technologie. Le facilitateur, pour rendre sa tâche facile, a besoin des solutions de vote pour départager les décideurs afin d'arriver à des conclusions dans une prise de décision. Une procédure de vote consiste à déterminer à partir d’une méthode le vainqueur ou le gagnant d’un vote. Il y a plusieurs procédures de vote dont certaines sont difficiles à expliquer et qui peuvent élire différents candidats/options/alternatives proposées. Le meilleur choix est celui dont son élection est acceptée facilement par le groupe. Le vote dans la théorie du choix social est une discipline largement étudiée dont les principes sont souvent complexes et difficiles à expliquer lors d’une réunion de prise de décision. Les systèmes de recommandation sont de plus en plus populaires dans tous les domaines de science. Ils peuvent aider les utilisateurs qui n’ont pas suffisamment d’expérience ou de compétence nécessaires pour évaluer un nombre élevé de procédures de vote existantes. Un système de recommandation peut alléger le travail du facilitateur dans la recherche d’une procédure vote adéquate en fonction du contexte de prise de décisions. Le sujet de ce travail de recherche s’inscrit dans le champ de l’aide à la décision de groupe. La problématique consiste à contribuer au développement d’un système d’aide à la décision de groupe (Group Decision Support System : GDSS). La solution devra s’intégrer dans la plateforme logicielle actuellement développée à l’IRIT GRUS : GRoUp Support.
Facilitation is a central element in decision-making, especially when using new technology tools. The facilitator, to make his task easy, needs voting solutions to decide between decision-makers in order to reach conclusions in a decision-making process. A voting procedure consists of determining from a method the winner of a vote. There are several voting procedures, some of which are difficult to explain and which may elect different candidate/options/alternatives proposed. The best choice is the one whose election is easily accepted by the group. Voting in social choice theory is a widely studied discipline whose principles are often complex and difficult to explain at a decision-making meeting. Recommendation systems are becoming more and more popular in all fields of science. They can help users who do not have sufficient experience or competence to evaluate large numbers of existing voting procedures. A recommendation system can lighten the facilitator's workload in finding an appropriate voting procedure based on the decision-making context. The objective of this research work is to design such recommendation system. This work is in the field of group decision support. The issue is to contribute to the development of a Group Decision Support System (GDSS). The solution will have to be integrated into the software platform currently being developed at IRITGRUS: GRoUp Support.
Advisors/Committee Members: Zaraté, Pascale (thesis director), Tangara, Fana (thesis director).
Subjects/Keywords: Procédures de vote; Voting procedures; Recommendation systems; Decision support systems
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APA ·
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CSE |
Export
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APA (6th Edition):
Coulibaly, A. (2019). Décision de groupe, Aide à la facilitation : ajustement de procédure de vote selon le contexte de décision : Group decision, Facilitation assistance : Adjustment of voting procedure according to the context of the decision. (Doctoral Dissertation). Université Toulouse I – Capitole. Retrieved from http://www.theses.fr/2019TOU10011
Chicago Manual of Style (16th Edition):
Coulibaly, Adama. “Décision de groupe, Aide à la facilitation : ajustement de procédure de vote selon le contexte de décision : Group decision, Facilitation assistance : Adjustment of voting procedure according to the context of the decision.” 2019. Doctoral Dissertation, Université Toulouse I – Capitole. Accessed March 06, 2021.
http://www.theses.fr/2019TOU10011.
MLA Handbook (7th Edition):
Coulibaly, Adama. “Décision de groupe, Aide à la facilitation : ajustement de procédure de vote selon le contexte de décision : Group decision, Facilitation assistance : Adjustment of voting procedure according to the context of the decision.” 2019. Web. 06 Mar 2021.
Vancouver:
Coulibaly A. Décision de groupe, Aide à la facilitation : ajustement de procédure de vote selon le contexte de décision : Group decision, Facilitation assistance : Adjustment of voting procedure according to the context of the decision. [Internet] [Doctoral dissertation]. Université Toulouse I – Capitole; 2019. [cited 2021 Mar 06].
Available from: http://www.theses.fr/2019TOU10011.
Council of Science Editors:
Coulibaly A. Décision de groupe, Aide à la facilitation : ajustement de procédure de vote selon le contexte de décision : Group decision, Facilitation assistance : Adjustment of voting procedure according to the context of the decision. [Doctoral Dissertation]. Université Toulouse I – Capitole; 2019. Available from: http://www.theses.fr/2019TOU10011
18.
Silva, Edeilson Milhomem da.
SWEETS: um sistema de recomendação de especialistas aplicado a redes sociais
.
Degree: 2009, Universidade Federal de Pernambuco
URL: http://repositorio.ufpe.br/handle/123456789/1835
► As organizações, com o intuito de aumentarem o seu grau de competitividade no mercado, vêm a cada instante buscando novas formas de evoluir a produtividade…
(more)
▼ As organizações, com o intuito de aumentarem o seu grau de competitividade
no mercado, vêm a cada instante buscando novas formas de evoluir a
produtividade e a qualidade dos produtos desenvolvidos, além da diminuição
de custos que está diretamente relacionada ao aumento do faturamento
líquido. Para que tais objetivos possam ser alcançados é primordial explorar
ao máximo o potencial de seus colaboradores e os possíveis relacionamentos
que esses colaboradores têm uns com os outros, ou seja, encontrar e partilhar
conhecimento tácito. Como o conhecimento tático está na mente das pessoas,
é difícil de ser formalizado e documentado, por isso, o ideal seria identificar e
recomendar a pessoa que detém o conhecimento.
Diante disso, a presente dissertação apresenta o Sistema de
Recomendação de Especialistas SWEETS e a sua implantação no ambiente
a.m.i.g.o.s., uma plataforma de gestão de conhecimento baseada em
conceitos voltados às redes sociais. O SWEETS foi desenvolvido em duas
versões, 1.0 e 2.0. A versão 1.0, de forma pró-ativa, aproxima pessoas com
especialidades em comum, ora pelos seus conhecimentos (perfil de escrita),
ora pelos seus interesses (perfil de leitura). Já a versão 2.0 do SWEETS não
atua de forma pró-ativa, ou seja, é necessário que haja a requisição de um
usuário especialista em determinada área, e é baseada em folksonomia para
extração de uma ontologia, fundamental para identificar as especialidades das
pessoas de forma mais eficaz. Esta ontologia é refletida pela co-ocorrência
das tags (conceitos) em relação aos itens (instâncias) e é independente de
domínio principal contribuição dessa dissertação.
A implantação do SWEETS no a.m.i.g.o.s. visa trazer benefícios como:
minimizar o problema de comunicação na corporação, prover um incentivo ao
conhecimento social e partilhar conhecimento; proporcionando, assim, à
empresa, a utilização mais eficaz dos conhecimentos de seus colaboradores
Advisors/Committee Members: Meira, Silvio Romero de Lemos (advisor).
Subjects/Keywords: Folksonomy;
Web-Based Social Network;
Recommendation systems;
Ontology.
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APA ·
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APA (6th Edition):
Silva, E. M. d. (2009). SWEETS: um sistema de recomendação de especialistas aplicado a redes sociais
. (Thesis). Universidade Federal de Pernambuco. Retrieved from http://repositorio.ufpe.br/handle/123456789/1835
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Silva, Edeilson Milhomem da. “SWEETS: um sistema de recomendação de especialistas aplicado a redes sociais
.” 2009. Thesis, Universidade Federal de Pernambuco. Accessed March 06, 2021.
http://repositorio.ufpe.br/handle/123456789/1835.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Silva, Edeilson Milhomem da. “SWEETS: um sistema de recomendação de especialistas aplicado a redes sociais
.” 2009. Web. 06 Mar 2021.
Vancouver:
Silva EMd. SWEETS: um sistema de recomendação de especialistas aplicado a redes sociais
. [Internet] [Thesis]. Universidade Federal de Pernambuco; 2009. [cited 2021 Mar 06].
Available from: http://repositorio.ufpe.br/handle/123456789/1835.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Silva EMd. SWEETS: um sistema de recomendação de especialistas aplicado a redes sociais
. [Thesis]. Universidade Federal de Pernambuco; 2009. Available from: http://repositorio.ufpe.br/handle/123456789/1835
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Dalhousie University
19.
Tanta-ngai, Hathai.
SHRACK: A SELF-ORGANIZING PEER-TO-PEER SYSTEM FOR DOCUMENT
SHARING AND TRACKING.
Degree: PhD, Faculty of Computer Science, 2010, Dalhousie University
URL: http://hdl.handle.net/10222/12814
► Given a set of peers with overlapping interests where each peer wishes to keep track of new documents that are relevant to their interests, we…
(more)
▼ Given a set of peers with overlapping interests where
each peer wishes to keep track of new documents that are relevant
to their interests, we propose Shrack-a self-organizing
peer-to-peer (P2P) system for document sharing and tracking. The
goal of a document-tracking system is to disseminate new documents
as they are published. We present a framework of Shrack and propose
a gossip-like pull-only information dissemination protocol. We
explore and develop mechanisms to enable a self-organizing network,
based on common interest of document sets among peers. Shrack peers
collaboratively share new documents of interest with other peers.
Interests of peers are modeled using relevant document sets and are
represented as peer profiles. There is no explicit pro file
exchange between peers and no global information available. We
describe how peers create their user pro files, discover the
existence of other peers, locally learn about interest of other
peers, and finally form a self-organizing overlay network of peers
with common interests. Unlike most existing P2P file sharing
systems which serve their users by finding relevant documents based
on an instant query, Shrack is designed to help users that have
long-term interests to keep track of relevant documents that are
newly available in the system. The framework can be used as an
infrastructure for any kind of documents and data, but in this
thesis, we focus on research publications. We built an event-driven
simulation to evaluate the performance and behaviour of Shrack. We
model simulated users associated with peers after a subset of
authors in the ACM digital library metadata collection. The
experimental results demonstrate that the Shrack dissemination
protocol is scalable as the network size increases. In addition,
self-organizing overlay networks, where connections between peers
are based on common interests as captured by their associated
document sets, can help improve the relevance of documents received
by peers in terms of F-score over random peer networks. Moreover,
the resulting self-organizing networks have the characteristics of
social networks.
Advisors/Committee Members: Dr. Jimmy Huang (external-examiner), Dr. Malcolm Heywood (graduate-coordinator), Dr. Nick Cercone (thesis-reader), Dr. Nur Zincir-Heywood (thesis-reader), Dr. Evangelos E. Milios and Dr. Vlado Keselj (thesis-supervisor), Not Applicable (ethics-approval), Not Applicable (manuscripts), Not Applicable (copyright-release).
Subjects/Keywords: peer-to-peer systems; document tracking; self-organizing networks; recommendation
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APA ·
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Export
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APA (6th Edition):
Tanta-ngai, H. (2010). SHRACK: A SELF-ORGANIZING PEER-TO-PEER SYSTEM FOR DOCUMENT
SHARING AND TRACKING. (Doctoral Dissertation). Dalhousie University. Retrieved from http://hdl.handle.net/10222/12814
Chicago Manual of Style (16th Edition):
Tanta-ngai, Hathai. “SHRACK: A SELF-ORGANIZING PEER-TO-PEER SYSTEM FOR DOCUMENT
SHARING AND TRACKING.” 2010. Doctoral Dissertation, Dalhousie University. Accessed March 06, 2021.
http://hdl.handle.net/10222/12814.
MLA Handbook (7th Edition):
Tanta-ngai, Hathai. “SHRACK: A SELF-ORGANIZING PEER-TO-PEER SYSTEM FOR DOCUMENT
SHARING AND TRACKING.” 2010. Web. 06 Mar 2021.
Vancouver:
Tanta-ngai H. SHRACK: A SELF-ORGANIZING PEER-TO-PEER SYSTEM FOR DOCUMENT
SHARING AND TRACKING. [Internet] [Doctoral dissertation]. Dalhousie University; 2010. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/10222/12814.
Council of Science Editors:
Tanta-ngai H. SHRACK: A SELF-ORGANIZING PEER-TO-PEER SYSTEM FOR DOCUMENT
SHARING AND TRACKING. [Doctoral Dissertation]. Dalhousie University; 2010. Available from: http://hdl.handle.net/10222/12814
University of California – Riverside
20.
Elsisy, Amr.
Exploring Temporal Context for Collaborative Filtering.
Degree: Computer Science, 2017, University of California – Riverside
URL: http://www.escholarship.org/uc/item/3dx874qn
► Thousands of new users join social media website everyday, generating huge amounts of new data. Twitter users for example, generate millions of new posts per…
(more)
▼ Thousands of new users join social media website everyday, generating huge amounts of new data. Twitter users for example, generate millions of new posts per day. This can flood our users with huge amounts of information, and thus overload them with information that for the most part they are not interested in. To fix this problem, we need to only show our users information relative to them, such as posts from people they are following. This thesis focuses on how to make accurate recommendations to each user, on which users/pages to follow, thus helping the user view information that is important to them. In particular, we focus on exploring the following research questions: 1) which features yield the best recommendation accuracy, and 2) given those features, what is the best granularity for them, that captures the underlying dynamics, leading to high accuracy.
Subjects/Keywords: Computer science; Collaborative; Context; Filtering; Recommendation; Systems; Temporal
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APA ·
Chicago ·
MLA ·
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CSE |
Export
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Manager
APA (6th Edition):
Elsisy, A. (2017). Exploring Temporal Context for Collaborative Filtering. (Thesis). University of California – Riverside. Retrieved from http://www.escholarship.org/uc/item/3dx874qn
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Elsisy, Amr. “Exploring Temporal Context for Collaborative Filtering.” 2017. Thesis, University of California – Riverside. Accessed March 06, 2021.
http://www.escholarship.org/uc/item/3dx874qn.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Elsisy, Amr. “Exploring Temporal Context for Collaborative Filtering.” 2017. Web. 06 Mar 2021.
Vancouver:
Elsisy A. Exploring Temporal Context for Collaborative Filtering. [Internet] [Thesis]. University of California – Riverside; 2017. [cited 2021 Mar 06].
Available from: http://www.escholarship.org/uc/item/3dx874qn.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Elsisy A. Exploring Temporal Context for Collaborative Filtering. [Thesis]. University of California – Riverside; 2017. Available from: http://www.escholarship.org/uc/item/3dx874qn
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
UCLA
21.
Hsieh, Cheng-kang.
Small Data Systems.
Degree: Computer Science, 2016, UCLA
URL: http://www.escholarship.org/uc/item/2rd9m1r2
► A myriad of digital services mediate our daily lives. As a result, we are continuously generating digital traces, referred to as small data, that can…
(more)
▼ A myriad of digital services mediate our daily lives. As a result, we are continuously generating digital traces, referred to as small data, that can be used to chronicle, characterize, and influence our behaviors and preferences. This thesis is focused on the notion of small data systems – the services and tools designed for individuals to more directly and personally leverage their collective small data. We develop novel algorithms, toolsets and the system infrastructure to address cross-cutting challenges in small data systems. We evaluate the efficacy and feasibility of our contributions with real-world datasets, system deployments, and user studies in two application contexts: (1) Lifestreams, a toolset to facilitate the exploration of small data for personal behavioral analysis and chronic disease prevention, and (2) Immersive Recommendations, a new recommendation paradigm using small data to enable effective personalization for online services across the web.
Subjects/Keywords: Computer science; immersive recommendations; mobile health; personalization; recommendation systems; small data
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APA ·
Chicago ·
MLA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hsieh, C. (2016). Small Data Systems. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/2rd9m1r2
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Hsieh, Cheng-kang. “Small Data Systems.” 2016. Thesis, UCLA. Accessed March 06, 2021.
http://www.escholarship.org/uc/item/2rd9m1r2.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Hsieh, Cheng-kang. “Small Data Systems.” 2016. Web. 06 Mar 2021.
Vancouver:
Hsieh C. Small Data Systems. [Internet] [Thesis]. UCLA; 2016. [cited 2021 Mar 06].
Available from: http://www.escholarship.org/uc/item/2rd9m1r2.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Hsieh C. Small Data Systems. [Thesis]. UCLA; 2016. Available from: http://www.escholarship.org/uc/item/2rd9m1r2
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
22.
Τουλιάτος, Γεράσιμος.
Ανάπτυξη συστήματος παροχής συστάσεων με χρήση τεχνικών σημασιολογικής ανάλυσης.
Degree: 2012, University of Patras
URL: http://hdl.handle.net/10889/6141
► Εξαιτίας του μεγάλου όγκου δεδομένων που υπάρχουν στον Παγκόσμιο Ιστό, η ανεύρεση της επιθυμητής πληροφορίας από ένα χρήστη μπορεί να αποδειχθεί χρονοβόρα. Διάφορα συστήματα προσωποποιημένης…
(more)
▼ Εξαιτίας του μεγάλου όγκου δεδομένων που υπάρχουν στον Παγκόσμιο Ιστό, η ανεύρεση της επιθυμητής πληροφορίας από ένα χρήστη μπορεί να αποδειχθεί χρονοβόρα. Διάφορα συστήματα προσωποποιημένης αναζήτησης έχουν προταθεί κατά καιρούς για να διευκολύνουν την επίλυση του συγκεκριμένου προβλήματος. Στόχος της παρούσας εργασίας ήταν η μελέτη διάφορων τεχνικών βελτίωσης των αποτελεσμάτων μιας αναζήτησης και η ανάπτυξη ενός συστήματος που θα προβλέπει την πληροφοριακή ανάγκη ενός χρήστη και θα του προτείνει ένα σύνολο από σελίδες που πιθανόν να τον ικανοποιούν.
Επειδή το Web αποτελεί ένα πολύ μεγάλο σύστημα, η μελέτη μας ξεκινάει από το επίπεδο ιστοτόπου. Για την ανάπτυξη του συστήματός μας θα κάνουμε χρήση σημασιολογικών τεχνικών ανάλυσης. Πιο συγκεκριμένα, με χρήση μιας οντολογίας θα χαρακτηρίσουμε εννοιολογικά τις σελίδες ενός ιστοτόπου και επιπλέον θα χρησιμοποιήσουμε την οντολογία για να εκφράσουμε την πληροφοριακή ανάγκη του χρήστη.
Κατά την περιήγησή του στον ιστότοπο ο χρήστης επιλέγει εκείνους τους συνδέσμους που θεωρεί ότι το φέρνουν πιο κοντά στο στόχο του. Εμείς, χαρακτηρίζουμε κάθε υπερσύνδεσμο με έννοιες που συνδέονται με το περιεχόμενο της σελίδας στην οποία αυτός δείχνει. Επειδή, ο κάθε χρήστης αναπαριστά την πληροφορία με ένα δικό του δίκτυο εννοιών, υιοθετήσαμε μια οντολογία που συγκεντρώνει αυτό που ονομάζουμε “κοινή γνώση” για ένα θέμα. Χρησιμοποιώντας, τις έννοιες από τους υπερσυνδέσμους που επέλεξε ο χρήστης, τις σχέσεις μεταξύ των εννοιών της οντολογίας εκτιμούμε τις πιθανές έννοιες – στόχους του χρήστη και προσδιορίζουμε με αυτόν τον τρόπο την πληροφοριακή του ανάγκη. Τέλος, κατατάσσουμε τις σελίδες ως προς τη εννοιολογική τους συσχέτιση με τα ενδιαφέροντα του χρήστη και δημιουργούμε τις προτάσεις μας.
Due to the large volume of data available on the Web, finding the desired information can be time consuming. Various personalized search systems have been proposed to help resolve this problem. The aim of this work was to study various techniques used to deal with the problem and also, develop a system that will predict a user's information need and propose a set of pages that might satisfy him.
Because the Web is a very large system, our study starts at the level of a site. In developing our system we will make use of semantic analysis techniques. Specifically, we will use an ontology to describe the contents of the pages of a website and we will also use the ontology to express the information need of the user.
While browsing, the user selects those links, that considers they will bring him closer to his goal. We characterize each link with concepts associated with the content of the page they point to. Because each user represents the information in his own concept network, we adopted an ontology to express what is said to be 'common knowledge' on a topic. Using the concepts of the hyperlinks that the user selected and the relations between the concepts of the ontology, we choose the possible concept that user has in mind, and thus determine his information needs. Finally, we…
Advisors/Committee Members: Γαροφαλάκης, Ιωάννης, Touliatos, Gerasimos, Γαροφαλάκης, Ιωάννης, Μακρής, Χρήστος, Χατζηλυγερούδης, Ιωάννης.
Subjects/Keywords: Συστήματα παροχής συστάσεων; Σημασιολογική ανάλυση; 025.042 5; Recommendation systems; Semantic analysis
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APA (6th Edition):
Τουλιάτος, . (2012). Ανάπτυξη συστήματος παροχής συστάσεων με χρήση τεχνικών σημασιολογικής ανάλυσης. (Masters Thesis). University of Patras. Retrieved from http://hdl.handle.net/10889/6141
Chicago Manual of Style (16th Edition):
Τουλιάτος, Γεράσιμος. “Ανάπτυξη συστήματος παροχής συστάσεων με χρήση τεχνικών σημασιολογικής ανάλυσης.” 2012. Masters Thesis, University of Patras. Accessed March 06, 2021.
http://hdl.handle.net/10889/6141.
MLA Handbook (7th Edition):
Τουλιάτος, Γεράσιμος. “Ανάπτυξη συστήματος παροχής συστάσεων με χρήση τεχνικών σημασιολογικής ανάλυσης.” 2012. Web. 06 Mar 2021.
Vancouver:
Τουλιάτος . Ανάπτυξη συστήματος παροχής συστάσεων με χρήση τεχνικών σημασιολογικής ανάλυσης. [Internet] [Masters thesis]. University of Patras; 2012. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/10889/6141.
Council of Science Editors:
Τουλιάτος . Ανάπτυξη συστήματος παροχής συστάσεων με χρήση τεχνικών σημασιολογικής ανάλυσης. [Masters Thesis]. University of Patras; 2012. Available from: http://hdl.handle.net/10889/6141
Virginia Commonwealth University
23.
Greco, Chase D.
A Behavior-Driven Recommendation System for Stack Overflow Posts.
Degree: MS, Computer Science, 2018, Virginia Commonwealth University
URL: https://doi.org/10.25772/PK07-XV60
;
https://scholarscompass.vcu.edu/etd/5396
► Developers are often tasked with maintaining complex systems. Regardless of prior experience, there will inevitably be times in which they must interact with parts…
(more)
▼ Developers are often tasked with maintaining complex
systems. Regardless of prior experience, there will inevitably be times in which they must interact with parts of the system with which they are unfamiliar. In such cases,
recommendation systems may serve as a valuable tool to assist the developer in implementing a solution. Many
recommendation systems in software engineering utilize the Stack Overflow knowledge-base as the basis of forming their recommendations. Traditionally, these
systems have relied on the developer to explicitly invoke them, typically in the form of specifying a query. However, there may be cases in which the developer is in need of a
recommendation but unaware that their need exists. A new class of
recommendation systems deemed Behavior-Driven
Recommendation Systems for Software Engineering seeks to address this issue by relying on developer behavior to determine when a
recommendation is needed, and once such a determination is made, formulate a search query based on the software engineering task context. This thesis presents one such system, StackInTheFlow, a plug-in integrating into the IntelliJ family of Java IDEs. StackInTheFlow allows the user to intervi act with it as a traditional
recommendation system, manually specifying queries and browsing returned Stack Overflow posts. However, it also provides facilities for detecting when the developer is in need of a
recommendation, defined when the developer has encountered an error messages or a difficulty detection model based on indicators of developer progress is fired. Once such a determination has been made, a query formulation model constructed based on a periodic data dump of Stack Overflow posts will automatically form a query from the software engineering task context extracted from source code currently open within the IDE. StackInTheFlow also provides mechanisms to personalize, over time, the results displayed to a specific set of Stack Overflow tags based on the results previously selected by the user. The effectiveness of these mechanisms are examined and results based the collection of anonymous user logs and a small scale study are presented. Based on the results of these evaluations, it was found that some of the queries issued by the tool are effective, however there are limitations regarding the extraction of the appropriate context of the software engineering task yet to overcome.
Advisors/Committee Members: Kostadin Damevski.
Subjects/Keywords: Stack Overflow; Recommendation Systems; IDE; Developer Tools; Software Engineering
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APA (6th Edition):
Greco, C. D. (2018). A Behavior-Driven Recommendation System for Stack Overflow Posts. (Thesis). Virginia Commonwealth University. Retrieved from https://doi.org/10.25772/PK07-XV60 ; https://scholarscompass.vcu.edu/etd/5396
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Greco, Chase D. “A Behavior-Driven Recommendation System for Stack Overflow Posts.” 2018. Thesis, Virginia Commonwealth University. Accessed March 06, 2021.
https://doi.org/10.25772/PK07-XV60 ; https://scholarscompass.vcu.edu/etd/5396.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Greco, Chase D. “A Behavior-Driven Recommendation System for Stack Overflow Posts.” 2018. Web. 06 Mar 2021.
Vancouver:
Greco CD. A Behavior-Driven Recommendation System for Stack Overflow Posts. [Internet] [Thesis]. Virginia Commonwealth University; 2018. [cited 2021 Mar 06].
Available from: https://doi.org/10.25772/PK07-XV60 ; https://scholarscompass.vcu.edu/etd/5396.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Greco CD. A Behavior-Driven Recommendation System for Stack Overflow Posts. [Thesis]. Virginia Commonwealth University; 2018. Available from: https://doi.org/10.25772/PK07-XV60 ; https://scholarscompass.vcu.edu/etd/5396
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Brigham Young University
24.
Pera, Maria Soledad.
Using Online Data Sources to Make Recommendations on Reading Material for K-12 and Advanced Readers.
Degree: PhD, 2014, Brigham Young University
URL: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=5377&context=etd
► Reading is a fundamental skill that each person needs to develop during early childhood and continue to enhance into adulthood. While children/teenagers depend on…
(more)
▼ Reading is a fundamental skill that each person needs to develop during early childhood and continue to enhance into adulthood. While children/teenagers depend on this skill to advance academically and become educated individuals, adults are expected to acquire a certain level of proficiency in reading so that they can engage in social/civic activities and successfully participate in the workforce. A step towards assisting individuals to become lifelong readers is to provide them adequate reading selections which can cultivate their intellectual and emotional growth. Turning to (web) search engines for such reading choices can be overwhelming, given the huge volume of reading materials offered as a result of a search. An alternative is to rely on reading materials suggested by existing recommendation systems, which unfortunately are not capable of simultaneously matching the information needs, preferences, and reading abilities of individual readers. In this dissertation, we present novel recommendation strategies which identify appealing reading materials that the readers can comprehend, which in turn can motivate them to read. In accomplishing this task, we have examined used-defined data, in addition to information retrieved/inferred from reputable and freely-accessible online sources. We have incorporated the concept of “social trust” when making recommendations for advanced readers and suggested fiction books that match the reading ability of individual K-12 readers using our readability-analysis tool for books. Furthermore, we have emulated the readers' advisory service offered at school/public libraries in making recommendations for K-12 readers, which can be applied to advanced readers as well. A major contribution of our work is in the development of unsupervised recommendation strategies for advanced readers which suggest reading materials for both entertainment and learning acquisition purposes. Unlike their counterparts, these recommendation strategies are unaffected by the cold-start or long-tail problems, since they exploit user-defined data (if available) while taking advantage of alternative publicly-available metadata. Our readability-analysis tool is innovative, which can predict the readability-levels of books on-the-fly, even in the absence of excerpts from books, a task that cannot be accomplished by any of the well-known readability tools/strategies. Moreover, our multi-dimensional recommendation strategy is novel, since it simultaneously analyzes the reading abilities of K-12 readers, which books readers enjoy, why the books are appealing to them, and what subject matters the readers favor. Besides assisting K-12 readers, our recommender can be used by parents/teachers/librarians in locating reading materials to be suggested to their (K-12) children/students/patrons. We have validated the performance of each methodology presented in this dissertation using existing benchmark datasets or datasets we created for the evaluation purpose (which is another contribution we make to the research…
Subjects/Keywords: Recommendation Systems; Readability; K-12; Readers' Advisory; Computer Sciences
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APA (6th Edition):
Pera, M. S. (2014). Using Online Data Sources to Make Recommendations on Reading Material for K-12 and Advanced Readers. (Doctoral Dissertation). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=5377&context=etd
Chicago Manual of Style (16th Edition):
Pera, Maria Soledad. “Using Online Data Sources to Make Recommendations on Reading Material for K-12 and Advanced Readers.” 2014. Doctoral Dissertation, Brigham Young University. Accessed March 06, 2021.
https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=5377&context=etd.
MLA Handbook (7th Edition):
Pera, Maria Soledad. “Using Online Data Sources to Make Recommendations on Reading Material for K-12 and Advanced Readers.” 2014. Web. 06 Mar 2021.
Vancouver:
Pera MS. Using Online Data Sources to Make Recommendations on Reading Material for K-12 and Advanced Readers. [Internet] [Doctoral dissertation]. Brigham Young University; 2014. [cited 2021 Mar 06].
Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=5377&context=etd.
Council of Science Editors:
Pera MS. Using Online Data Sources to Make Recommendations on Reading Material for K-12 and Advanced Readers. [Doctoral Dissertation]. Brigham Young University; 2014. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=5377&context=etd
Blekinge Institute of Technology
25.
Kang, Li.
Automated Duplicate Bug Reports Detection - An Experiment at Axis Communication AB.
Degree: 2017, Blekinge Institute of Technology
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15399
► Context. Bug tracking systems play an important role in software maintenance. They allow users to submit bug reports. However, it has been observed that…
(more)
▼ Context. Bug tracking
systems play an important role in software maintenance. They allow users to submit bug reports. However, it has been observed that often a bug report submitted is a duplicate (when several users submit bug reports for the same problem, these reports are called duplicated issue reports) which results in considerable duplicate bug reports in a bug tracking system. Solutions for automating the process of duplicate bug reports detection can increase the productivity of software maintenance activities, as new incoming bug reports are directly compared with the existing bug reports to identify their similar bug reports, which is no need for the human to spend time reading, understanding, and searching. Although recently there has been considerable research on such solutions, there is still much room for improvement regarding accuracy and recall rate during the duplicate detection process. Besides, very few tools were evaluated in an industrial setting. Objectives. In this study, firstly, we aim to characterize automated duplicate bug report detection methods by exploring categories of all those methods, identifying proposed evaluation methods, specifying performance difference between the categories of methods. Then we propose a method leveraging recent advances on using semantic model – Doc2vec and present an overall framework - preprocessing, training a semantic model, calculating and ranking similarity, and retrieving duplicate bug reports of the proposed method. Finally, we apply an experiment to evaluate the performance of the proposed method and compare it with the selected best methods for the task of duplicate bug report detection Methods. To classify and analyze all existing research on automated duplicate bug report detection, we conducted a systematic mapping study. To evaluate our proposed method, we conducted an experiment with an identified number of bug reports on the internal bug report database of Axis Communication AB. Results. We classified automated duplicate bug report detection techniques into three categories - TOP N
recommendation and ranking approach, binary classification approach, and decision-making approach. We found that
[email protected] is the most common evaluation metric, and found that TOP N
recommendation and ranking approach has the best performance among the identified approaches. The experimental results showed that the recall rate of our proposed approach is significantly higher than the combination of TF-IDF with Word2vec and the combination of TF-IDF with LSI. Our combination of Doc2vec and TF-IDF approach, has a recall
[email protected] of 18.66%-42.88% in the TROUBLE data, which is an improvement of 1.63%-9.42% to the state-of-art. Conclusions. In this thesis, we identified and classified 44 automated duplicate bug report detection research papers by conducting a systematic mapping study. We provide an overview of the state-of-art, identifying…
Subjects/Keywords: Similar Bugs; Paragraph Vector; Information Retrieval; Recommendation Systems; Software Engineering; Programvaruteknik
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Manager
APA (6th Edition):
Kang, L. (2017). Automated Duplicate Bug Reports Detection - An Experiment at Axis Communication AB. (Thesis). Blekinge Institute of Technology. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15399
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Kang, Li. “Automated Duplicate Bug Reports Detection - An Experiment at Axis Communication AB.” 2017. Thesis, Blekinge Institute of Technology. Accessed March 06, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15399.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Kang, Li. “Automated Duplicate Bug Reports Detection - An Experiment at Axis Communication AB.” 2017. Web. 06 Mar 2021.
Vancouver:
Kang L. Automated Duplicate Bug Reports Detection - An Experiment at Axis Communication AB. [Internet] [Thesis]. Blekinge Institute of Technology; 2017. [cited 2021 Mar 06].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15399.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Kang L. Automated Duplicate Bug Reports Detection - An Experiment at Axis Communication AB. [Thesis]. Blekinge Institute of Technology; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15399
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
26.
Lu, Feng (author).
Enhancing the Diversity Adjusting Strategy with Personality Information in Music Recommender Systems.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:0a6bd5ab-b650-4707-a707-6603248d6ec4
► Current research on personality and diversity based Recommender Systems (RecSys) are mostly separated. In most diversity-based Recommender Systems, researchers usually endeavored to achieve an optimal…
(more)
▼ Current research on personality and diversity based Recommender
Systems (RecSys) are mostly separated. In most diversity-based Recommender
Systems, researchers usually endeavored to achieve an optimal balance between accuracy and diversity while they commonly set a same diversity level for all users. Different diversity needs for users with different personalities are rarely studied. Another branch of research on personality-based Recommender
Systems mostly emphasize utilizing personality information to enhancing the rating prediction accuracy so as to solve the ’Cold-Start Problem’. While few of them have in depth investigated whether and how it influences users’ other preference needs (such as diversity needs). This thesis presents the work how we combine these two branches of research together. Anchored in the music domain, we investigate how personality information can be incorporated into the Music Recommender
Systems to help adjust the diversity degrees for people with different personalities. We first conducted a pilot study to investigate the correlation between users’ personality factors and their diversity needs on the music recommendations. Results showed that there exits significant correlations between them, especially when we consider the personality factor ’Emotional Stability’. Based on such findings, we then proposed a personality-based diversification algorithm to help enhance the diversity adjusting strategy according to people’s personality information in music recommendations. Our offline and online evaluation results demonstrated that our proposed method is an effective solution to generate personalized
recommendation lists with relatively higher diversity.
Advisors/Committee Members: Tintarev, Nava (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: Recommender Systems; Diversity; Personality; Re-ranking; Music Recommendation
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APA (6th Edition):
Lu, F. (. (2018). Enhancing the Diversity Adjusting Strategy with Personality Information in Music Recommender Systems. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0a6bd5ab-b650-4707-a707-6603248d6ec4
Chicago Manual of Style (16th Edition):
Lu, Feng (author). “Enhancing the Diversity Adjusting Strategy with Personality Information in Music Recommender Systems.” 2018. Masters Thesis, Delft University of Technology. Accessed March 06, 2021.
http://resolver.tudelft.nl/uuid:0a6bd5ab-b650-4707-a707-6603248d6ec4.
MLA Handbook (7th Edition):
Lu, Feng (author). “Enhancing the Diversity Adjusting Strategy with Personality Information in Music Recommender Systems.” 2018. Web. 06 Mar 2021.
Vancouver:
Lu F(. Enhancing the Diversity Adjusting Strategy with Personality Information in Music Recommender Systems. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Mar 06].
Available from: http://resolver.tudelft.nl/uuid:0a6bd5ab-b650-4707-a707-6603248d6ec4.
Council of Science Editors:
Lu F(. Enhancing the Diversity Adjusting Strategy with Personality Information in Music Recommender Systems. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:0a6bd5ab-b650-4707-a707-6603248d6ec4
Delft University of Technology
27.
Knyazev, Norman (author).
Modelling Time Delta in User-Item Interactions Using Deep Recommender Systems.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:34ffc4b0-6c61-4649-a628-9d1f7b9fa846
► Many widely used Recommender System algorithms estimate user tastes without accounting for their evolving nature. In recent years there has been a gradual increase in…
(more)
▼ Many widely used Recommender System algorithms estimate user tastes without accounting for their evolving nature. In recent years there has been a gradual increase in methods incorporating such temporal dynamics through sequential processing of user consumption histories. Some works have also included additional temporal features such as time stamps and intervals between a given user’s interactions with the platform. The latter, in particular, may be a strong signal providing additional context with respect to the current user preferences. However, in previous works this source of information has only been used passingly, without any significant analysis of its impact on
recommendation. In this thesis we examine the effects of such intervals, termed time gaps, on
recommendation accuracy. In order to do so, we propose a family of novel DeepTimeDelta models, extending a state-of-the-art sequential Recurrent Neural Network based recommender. Through the comparison of our time-dependent models to the sequential baseline we demonstrate that the use of time gaps leads to improvements in
recommendation performance, in particular for cases following longer user inactivity. Furthermore, we examine the mechanisms regulating the model
recommendation behaviour. Our results suggest that the above performance improvements may be achieved through increased reliance on user long term preferences as well as strong regulation of the importance of the recently consumed items. Finally, we examine the performance differences for users groups with distinct consumption behaviours, demonstrating some improvement for groups featuring less active users as well as users consuming more popular content.
Advisors/Committee Members: Hanjalic, Alan (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: Recommender Systems; Deep Learning; Time gaps; Temporal recommendation
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APA (6th Edition):
Knyazev, N. (. (2020). Modelling Time Delta in User-Item Interactions Using Deep Recommender Systems. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:34ffc4b0-6c61-4649-a628-9d1f7b9fa846
Chicago Manual of Style (16th Edition):
Knyazev, Norman (author). “Modelling Time Delta in User-Item Interactions Using Deep Recommender Systems.” 2020. Masters Thesis, Delft University of Technology. Accessed March 06, 2021.
http://resolver.tudelft.nl/uuid:34ffc4b0-6c61-4649-a628-9d1f7b9fa846.
MLA Handbook (7th Edition):
Knyazev, Norman (author). “Modelling Time Delta in User-Item Interactions Using Deep Recommender Systems.” 2020. Web. 06 Mar 2021.
Vancouver:
Knyazev N(. Modelling Time Delta in User-Item Interactions Using Deep Recommender Systems. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 06].
Available from: http://resolver.tudelft.nl/uuid:34ffc4b0-6c61-4649-a628-9d1f7b9fa846.
Council of Science Editors:
Knyazev N(. Modelling Time Delta in User-Item Interactions Using Deep Recommender Systems. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:34ffc4b0-6c61-4649-a628-9d1f7b9fa846
University of Western Ontario
28.
Yew, Chern Har.
Architecture Supporting Computational Trust Formation.
Degree: 2011, University of Western Ontario
URL: https://ir.lib.uwo.ca/etd/86
► Trust is a concept that has been used in computing to support better decision making. For example, trust can be used in access control. Trust…
(more)
▼ Trust is a concept that has been used in computing to support better decision making. For example, trust can be used in access control. Trust can also be used to support service selection. Although certain elements of trust such as reputation has gained widespread acceptance, a general model of trust has so far not seen widespread usage. This is due to the challenges of implementing a general trust model. In this thesis, a middleware based approach is proposed to address the implementation challenges.
The thesis proposes a general trust model known as computational trust. Computational trust is based on research in social psychology. An individual’s computational trust is formed with the support of the proposed computational trust architecture. The architecture consists of a middleware and middleware clients. The middleware can be viewed as a representation of the individual that shares its knowledge with all the middleware clients. Each application uses its own middleware client to form computational trust for its decision making needs. Computational trust formation can be adapted to changing circumstances. The thesis also proposed algorithms for computational trust formation. Experiments, evaluations and scenarios are also presented to demonstrate the feasibility of the middleware based approach to computational trust formation.
Subjects/Keywords: trust; experience; recommendation; reputation; belief; architecture; Systems Architecture
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APA (6th Edition):
Yew, C. H. (2011). Architecture Supporting Computational Trust Formation. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/86
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Yew, Chern Har. “Architecture Supporting Computational Trust Formation.” 2011. Thesis, University of Western Ontario. Accessed March 06, 2021.
https://ir.lib.uwo.ca/etd/86.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Yew, Chern Har. “Architecture Supporting Computational Trust Formation.” 2011. Web. 06 Mar 2021.
Vancouver:
Yew CH. Architecture Supporting Computational Trust Formation. [Internet] [Thesis]. University of Western Ontario; 2011. [cited 2021 Mar 06].
Available from: https://ir.lib.uwo.ca/etd/86.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Yew CH. Architecture Supporting Computational Trust Formation. [Thesis]. University of Western Ontario; 2011. Available from: https://ir.lib.uwo.ca/etd/86
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
29.
Ghanmode, Ishan (author).
Leveraging Social Information To Improve Recommendation Novelty.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:573aecf0-8d1d-4c9b-849a-58a47b6d07e5
► In today’s digital world, users are often confronted with an abundance of information. Whether the user is looking to compare online prices for products, searching…
(more)
▼ In today’s digital world, users are often confronted with an abundance of information. Whether the user is looking to compare online prices for products, searching for new movies to watch or music to listen, the available information at hand exceeds the amount of information which the user wants to consider before making a choice. For this, various
recommendation systems have been developed. Primarily, the recommendations made by these recommendations
systems have been evaluated based on their accuracy. More recent research has begun evaluating the subjective perception of the recommendations and has developed additional attributes such as diversity and novelty to evaluate
recommendation systems. However, the impact of such attributes on actual user satisfaction has been explored less. Recent research has seen an increase in evaluating other aspects of
recommendation systems such as
recommendation interfaces. Nonetheless, very limited work has been done in terms of
recommendation system interfaces to improve the perceived quality of the recommendations and overall user satisfaction. This master thesis introduces and evaluates a novel interface, MovieTweeters. It is a movie
recommendation system which incorporates social information with a traditional
recommendation algorithm to generate recommendations for users. Few previous studies have investigated the influence of using social information in interactive interfaces to improve the novelty of recommendations. To address this gap, we investigate whether social information can be incorporated effectively into an interactive interface to improve
recommendation novelty and user satisfaction. Our initial results suggest that such an interactive interface does indeed helps users discover more novel items. Also, we observed users who perceived that they discovered more novel and diverse items reported increased levels of user satisfaction. Surprisingly, we observed that even though we successfully were able to increase the system diversity of the recommendations, it had a negative correlation with users perception of novelty and diversity of the items highlighting the importance of improved user-centered approaches.
Advisors/Committee Members: Tintarev, Nava (mentor), Houben, Geert-Jan (graduation committee), Liem, Cynthia (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Social Recommendation Systems; Interactive User Interfaces; User Satisfaction; Novelty; Diversity
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APA (6th Edition):
Ghanmode, I. (. (2018). Leveraging Social Information To Improve Recommendation Novelty. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:573aecf0-8d1d-4c9b-849a-58a47b6d07e5
Chicago Manual of Style (16th Edition):
Ghanmode, Ishan (author). “Leveraging Social Information To Improve Recommendation Novelty.” 2018. Masters Thesis, Delft University of Technology. Accessed March 06, 2021.
http://resolver.tudelft.nl/uuid:573aecf0-8d1d-4c9b-849a-58a47b6d07e5.
MLA Handbook (7th Edition):
Ghanmode, Ishan (author). “Leveraging Social Information To Improve Recommendation Novelty.” 2018. Web. 06 Mar 2021.
Vancouver:
Ghanmode I(. Leveraging Social Information To Improve Recommendation Novelty. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Mar 06].
Available from: http://resolver.tudelft.nl/uuid:573aecf0-8d1d-4c9b-849a-58a47b6d07e5.
Council of Science Editors:
Ghanmode I(. Leveraging Social Information To Improve Recommendation Novelty. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:573aecf0-8d1d-4c9b-849a-58a47b6d07e5
University of Minnesota
30.
Kumar, Vikas.
Exploring the Balance Between Novelty and Familiarity in Recommendation Systems.
Degree: PhD, Computer Science, 2018, University of Minnesota
URL: http://hdl.handle.net/11299/201159
► The balance users seek between the comfort of familiar recommendations and the excitement they solicit in novel ones is a challenge for recommendation systems. On…
(more)
▼ The balance users seek between the comfort of familiar recommendations and the excitement they solicit in novel ones is a challenge for recommendation systems. On the one hand, familiar options help improve the trust and confidence of users in the system. On the other hand, novel options play a key role in providing serendipity - the delightful surprise that makes system more engaging and useful. However, in their pursuit to achieve the delicate balance, existing recommendation techniques have overlooked user-specific needs and assumed that users have the same, constant appetite for the amount of novelty and familiarity in their recommendations. This thesis highlights and emphasizes the dynamics in user consumption of familiar versus novel items and explores the balance between the two in recommendations. Studying users' consumption patterns in online music streaming we first show that users have distinct and dynamic appetites for novelty in their consumption. We show how a recommender adaptive to the varying appetite of users' novelty consumption is more accurate than traditional one-size-fits-all approaches. Second, we show that not only do users have a distinct appetite, but that there exists a systematic relationship between the novel items they consume and the time elapsed between successive sessions. Third, we address the limitations of inferences from activity logs and the assumptions we impose on actions taken by users in developing algorithms. Instead, we use a qualitative approach in which we interview users while they engage in music listening in their everyday environments to identify how a combination of factors, such as attention needs, exposure to artists or songs etc., influence the balance users seek between novelty and familiarity in their selection. Finally, apart from analysis of what users consume, this thesis also demonstrates the implications of individual familiarity and novelty on the content users produce in online social platforms. Analyzing online location-tagged photos shared by users on Flickr and the familiarity of the users with a location, we show that the locals, who are more familiar with a location, capture more diverse photos of the location, yet it is the tourists who, in their short stay and being less familiar, capture more representative photos of the location. The thesis aims to provide a guiding tool to define, measure, and model the dynamics of the familiarity and novelty balance users consume on online media platforms. The simplicity of our method and its ability to be embedded within existing recommendation techniques supports the contribution of this thesis as well as its general adaptability to other domains of user interactions.
Subjects/Keywords: adaptive recommendations; local and tourists; music recommendations; novelty; recommendation systems
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APA (6th Edition):
Kumar, V. (2018). Exploring the Balance Between Novelty and Familiarity in Recommendation Systems. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/201159
Chicago Manual of Style (16th Edition):
Kumar, Vikas. “Exploring the Balance Between Novelty and Familiarity in Recommendation Systems.” 2018. Doctoral Dissertation, University of Minnesota. Accessed March 06, 2021.
http://hdl.handle.net/11299/201159.
MLA Handbook (7th Edition):
Kumar, Vikas. “Exploring the Balance Between Novelty and Familiarity in Recommendation Systems.” 2018. Web. 06 Mar 2021.
Vancouver:
Kumar V. Exploring the Balance Between Novelty and Familiarity in Recommendation Systems. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2021 Mar 06].
Available from: http://hdl.handle.net/11299/201159.
Council of Science Editors:
Kumar V. Exploring the Balance Between Novelty and Familiarity in Recommendation Systems. [Doctoral Dissertation]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/201159
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