You searched for subject:(recommender systems).
Showing records 1 – 30 of
451 total matches.
◁ [1] [2] [3] [4] [5] … [16] ▶
Texas State University – San Marcos
1.
Channamsetty, Sushma.
Recommender Response to User Profile Diversity and Popularity Bias.
Degree: MS, Computer Science, 2016, Texas State University – San Marcos
URL: https://digital.library.txstate.edu/handle/10877/6313 
► Recommender systems are commonly evaluated to understand the effectiveness of their algorithms. Diversity and novelty of the recommender systems have been in consideration while evaluating…
(more)
▼ Recommender systems are commonly evaluated to understand the effectiveness of their algorithms. Diversity and novelty of the
recommender systems have been in consideration while evaluating the
systems in addition to accuracy and prediction metrics in order to provide better recommendations. Different evaluation metrics that are related to diversity and novelty have been discussed in some of the previous works. This work provides a comprehensive study and analysis of the
recommender algorithms and its relationship to the user’s bias in terms of popularity and diversity. This kind of analysis helps us to understand if the core algorithms personalize the recommendations based on the users’ bias. We performed offline experiments using the MovieLens data and analyzed the correlation between the user profile and the
recommender profile for both diversity and popularity bias using different metrics. Finally, we report the analysis observations and study how it complements the previous work done.
Advisors/Committee Members: Ekstrand, Michael (advisor), Hee Hiong Ngu, Anne (committee member), Metsis, Vangelis (committee member).
Subjects/Keywords: Recommender systems; Recommender; Recommender systems (Information filtering); Expert systems (Computer science)
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Channamsetty, S. (2016). Recommender Response to User Profile Diversity and Popularity Bias. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6313
Chicago Manual of Style (16th Edition):
Channamsetty, Sushma. “Recommender Response to User Profile Diversity and Popularity Bias.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed March 09, 2021.
https://digital.library.txstate.edu/handle/10877/6313.
MLA Handbook (7th Edition):
Channamsetty, Sushma. “Recommender Response to User Profile Diversity and Popularity Bias.” 2016. Web. 09 Mar 2021.
Vancouver:
Channamsetty S. Recommender Response to User Profile Diversity and Popularity Bias. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2021 Mar 09].
Available from: https://digital.library.txstate.edu/handle/10877/6313.
Council of Science Editors:
Channamsetty S. Recommender Response to User Profile Diversity and Popularity Bias. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6313
2.
Kosaraju, Sai Sri.
StreamER: Evaluation Framework For Streaming Recommender Systems.
Degree: Faculty of Technology and Society (TS), 2018, Malmö University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20279
► Recommender systems have gained a lot of popularity in recent times due to their application in the wide range of fields. Recommender systems are…
(more)
▼ Recommender systems have gained a lot of popularity in recent times due
to their application in the wide range of fields. Recommender systems are
intended to support users in finding the relevant items based on their interests
and preferences. Recommender algorithms proposed by researchers
evolved over time from simple matching recommendations to machine learning
algorithms. One such class of algorithms with increasing focus is on
called streaming recommender systems, these algorithms treat input data as
a stream of events and make recommendations. To evaluate the algorithms
that work with continuous data streams, stream-based evaluation techniques
are needed. So far, less interest is shown in the research so far on the evaluation
of recommender systems in streaming environments.
In this thesis, a simple evaluation framework named StreamER that evaluates
recommender algorithms that work on streaming data is proposed.
StreamER is intended for the rapid prototyping and evaluation of incremental
algorithms. StreamER is designed and implemented using object-oriented
architecture to make it more flexible and expandable. StreamER can be
configured via a configuration file, which can configure algorithms, metrics
and other properties individually. StreamER has inbuilt support for calculating
accuracy metrics, namely click-through rate, precision, and recall.
The popular-seller and random recommender are two algorithms supported
out of the box with StreamER. Evaluation of StreamER is performed via a
combination of hypothesis and manual evaluation. Results have matched the
proposed hypothesis, thereby successfully evaluating the proposed framework
StreamER.
Subjects/Keywords: Recommender Systems; Recommender Systems Evaluation; Streaming Recommender Systems; Recommender system metrics; Recommender systems evaluation tools; Engineering and Technology; Teknik och teknologier
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kosaraju, S. S. (2018). StreamER: Evaluation Framework For Streaming Recommender Systems. (Thesis). Malmö University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20279
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):
Kosaraju, Sai Sri. “StreamER: Evaluation Framework For Streaming Recommender Systems.” 2018. Thesis, Malmö University. Accessed March 09, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20279.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Kosaraju, Sai Sri. “StreamER: Evaluation Framework For Streaming Recommender Systems.” 2018. Web. 09 Mar 2021.
Vancouver:
Kosaraju SS. StreamER: Evaluation Framework For Streaming Recommender Systems. [Internet] [Thesis]. Malmö University; 2018. [cited 2021 Mar 09].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20279.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Kosaraju SS. StreamER: Evaluation Framework For Streaming Recommender Systems. [Thesis]. Malmö University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20279
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Technology, Sydney
3.
Hao, Peng.
Cross-domain recommender system through tag-based models.
Degree: 2018, University of Technology, Sydney
URL: http://hdl.handle.net/10453/125626
► Nowadays, data pertaining to clients are generated at such a rapid rate it is completely beyond the processing ability of a human, which leads to…
(more)
▼ Nowadays, data pertaining to clients are generated at such a rapid rate it is completely beyond the processing ability of a human, which leads to a problem called information explosion. How to quickly and automatically provide personalized choices for someone from a large collection of resources has become a key factor in determining the success of many commercial activities. In this context, recommender systems have been developed as a type of software that aims to predict and suggest items which are relevant to a specific user by analyzing the user’s previous interaction data with certain items. Recommender systems have a broad application in our daily life, such as product recommendation in Amazon, video and movie recommendation in Youtube, music recommendation in Spotify.
A fundamental brick in building most recommender systems is the collaborative filtering-based model, which has been widely adopted due to its outstanding performance and flexible deployment. However, this model together and its variations suffer from the so-called data sparsity problem, which results when user sonly rate a limited number of items. With the development of the transfer learning technique in recent years, cross-domain recommendation has emerged as an effective way to address data sparsity in recommender systems. The principle of cross-domain recommendation is to exploit knowledge from auxiliary source domains to assist recommendation making in a sparse target domain.
In the development of cross-domain recommender systems, the most important step is to build a bridge between the domains in order to transfer knowledge. This task becomes more challenging in disjoint domains where users and items in both domains are completely non-overlapping. In this respect, tags are studied and utilized to establish explicit correspondence between domains. However, how to effectively exploit tags to increase domain overlap and ultimate recommendation quality remains as an open challenge which needs to be addressed.
This thesis aims to develop novel tag-based cross-domain recommendation models in disjoint domains. First, it review the existing state-of-the-art techniques related to this research. It then provides three solutions by exploiting domain-specific tags, tag-inferred structural knowledge and tag semantics, respectively. To evaluate the proposed models, this thesis conducts a series of experiments on public datasets and compare them with state-of-the-art baseline approaches. The experimental results show the superior performance achieved by our models in different recommendation tasks under sparse settings. The findings of this research not only contribute to the state-of-the-art on cross-domain recommender systems, but also provide practical guidance for handling unstructured tag data in recommendation tasks.
Subjects/Keywords: Collaborative filtering-based model recommender systems.; Cross-domain recommender systems.; Recommender systems.
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hao, P. (2018). Cross-domain recommender system through tag-based models. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/125626
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):
Hao, Peng. “Cross-domain recommender system through tag-based models.” 2018. Thesis, University of Technology, Sydney. Accessed March 09, 2021.
http://hdl.handle.net/10453/125626.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Hao, Peng. “Cross-domain recommender system through tag-based models.” 2018. Web. 09 Mar 2021.
Vancouver:
Hao P. Cross-domain recommender system through tag-based models. [Internet] [Thesis]. University of Technology, Sydney; 2018. [cited 2021 Mar 09].
Available from: http://hdl.handle.net/10453/125626.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Hao P. Cross-domain recommender system through tag-based models. [Thesis]. University of Technology, Sydney; 2018. Available from: http://hdl.handle.net/10453/125626
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Alberta
4.
Bakhshinategh, Behdad.
Design of a Course Recommender System as an Application of
Collecting Graduating Attributes.
Degree: MS, Department of Computing Science, 2016, University of Alberta
URL: https://era.library.ualberta.ca/files/cnz805z87v
► In educational research, the term of Graduating Attributes has been used for the qualities, skills and understandings a university community agrees its students would develop.…
(more)
▼ In educational research, the term of Graduating
Attributes has been used for the qualities, skills and
understandings a university community agrees its students would
develop. Having a description of Graduating Attributes is one of
the ways through which universities can display the outcomes of
higher education. But can Graduating Attributes be used also to
enhance the process of learning? In this thesis, we discuss how
graduating attributes can be used in data mining applications to
improve the learning process. An example of a data mining
application can be a course recommender system which helps students
to choose the courses they would participate in. In our work we
have implemented this recommender system as an example of possible
applications which Graduating Attributes can provide. In order to
achieve such a goal we first needed to implement a tool for
assessing Graduating Attributes and gather data. In spite of
implementing this tool, we were not able to gather sufficient
amount of data. As a result, based on the structure of data in our
assessment tool, we have generated synthetic data which we have
used for the evaluation of the course recommender system. The
results of the recommendation improve over time as a result of
having more data. The mean squared error decreases from 0.32 in
second semester to 0.08 in the tenth semester.
Subjects/Keywords: Graduating Attributes; Recommender Systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bakhshinategh, B. (2016). Design of a Course Recommender System as an Application of
Collecting Graduating Attributes. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cnz805z87v
Chicago Manual of Style (16th Edition):
Bakhshinategh, Behdad. “Design of a Course Recommender System as an Application of
Collecting Graduating Attributes.” 2016. Masters Thesis, University of Alberta. Accessed March 09, 2021.
https://era.library.ualberta.ca/files/cnz805z87v.
MLA Handbook (7th Edition):
Bakhshinategh, Behdad. “Design of a Course Recommender System as an Application of
Collecting Graduating Attributes.” 2016. Web. 09 Mar 2021.
Vancouver:
Bakhshinategh B. Design of a Course Recommender System as an Application of
Collecting Graduating Attributes. [Internet] [Masters thesis]. University of Alberta; 2016. [cited 2021 Mar 09].
Available from: https://era.library.ualberta.ca/files/cnz805z87v.
Council of Science Editors:
Bakhshinategh B. Design of a Course Recommender System as an Application of
Collecting Graduating Attributes. [Masters Thesis]. University of Alberta; 2016. Available from: https://era.library.ualberta.ca/files/cnz805z87v
Texas A&M University
5.
Guo, Shiqiang.
ResuMatcher: A Personalized Resume-Job Matching System.
Degree: MS, Computer Science, 2015, Texas A&M University
URL: http://hdl.handle.net/1969.1/154963
► Today, online recruiting web sites such as Monster and Indeed.com have become one of the main channels for people to find jobs. These web platforms…
(more)
▼ Today, online recruiting web sites such as Monster and Indeed.com have become one of the main channels for people to find jobs. These web platforms have provided their services for more than ten years, and have saved a lot of time and money for both job seekers and organizations who want to hire people. However, traditional information retrieval techniques may not be appropriate for users. The reason is because the number of results returned to a job seeker may be huge, so job seekers are required to spend a significant amount of time reading and reviewing their options. One popular approach to resolve this difficulty for users are
recommender systems,
which is a technology that has been studied for a long time.
In this thesis we have made an effort to propose a personalized job-résumé matching system, which could help job seekers to find appropriate jobs more easily. We create a finite state transducer based information extraction library to extract models from résumés and job descriptions. We devised a new statistical-based ontology similarity measure to compare the résumé models and the job models. Since the most appropriate jobs will be returned first, the users of the system may get a better result than current job finding web sites. To evaluate the system, we computed Normalized Discounted Cumulative Gain (NDCG) and
[email protected] of our system, and compared to three other existing models as well as the live result from Indeed.com.
Advisors/Committee Members: Hammond, Tracy (advisor), Jiang, Anxiao (committee member), Goldberg, Daniel W. (committee member).
Subjects/Keywords: Job Search; Recommender Systems; NLP
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Guo, S. (2015). ResuMatcher: A Personalized Resume-Job Matching System. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/154963
Chicago Manual of Style (16th Edition):
Guo, Shiqiang. “ResuMatcher: A Personalized Resume-Job Matching System.” 2015. Masters Thesis, Texas A&M University. Accessed March 09, 2021.
http://hdl.handle.net/1969.1/154963.
MLA Handbook (7th Edition):
Guo, Shiqiang. “ResuMatcher: A Personalized Resume-Job Matching System.” 2015. Web. 09 Mar 2021.
Vancouver:
Guo S. ResuMatcher: A Personalized Resume-Job Matching System. [Internet] [Masters thesis]. Texas A&M University; 2015. [cited 2021 Mar 09].
Available from: http://hdl.handle.net/1969.1/154963.
Council of Science Editors:
Guo S. ResuMatcher: A Personalized Resume-Job Matching System. [Masters Thesis]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/154963
Brigham Young University
6.
Brinton, Derrick James.
Recommender Systems for Family History Source Discovery.
Degree: MS, 2017, Brigham Young University
URL: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=7606&context=etd
► As interest in family history research increases, greater numbers of amateurs are participating in genealogy. However, finding sources that provide useful information on individuals in…
(more)
▼ As interest in family history research increases, greater numbers of amateurs are participating in genealogy. However, finding sources that provide useful information on individuals in genealogical research is often an overwhelming task, even for experts. Many tools assist genealogists in their work, including many computer-based systems. Prior to this work, recommender systems had not yet been applied to genealogy, though their ability to navigate patterns in large amounts of data holds great promise for the genealogical domain. We create the Family History Source Recommender System to mimic human behavior in locating sources of genealogical information. The recommender system is seeded with existing source data from the FamilySearch database. The typical recommender systems algorithms are not designed for family history work, so we adjust them to fit the problem. In particular, recommendations are created for deceased individuals, with multiple users being able to consume the same recommendations. Additionally, our similarity computation takes into account as much information about individuals as possible in order to create connections that would otherwise not exist. We use offline n-fold cross-validation to validate the results. The system provides results with high accuracy.
Subjects/Keywords: Recommender Systems; Genealogy; Computer Sciences
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Brinton, D. J. (2017). Recommender Systems for Family History Source Discovery. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=7606&context=etd
Chicago Manual of Style (16th Edition):
Brinton, Derrick James. “Recommender Systems for Family History Source Discovery.” 2017. Masters Thesis, Brigham Young University. Accessed March 09, 2021.
https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=7606&context=etd.
MLA Handbook (7th Edition):
Brinton, Derrick James. “Recommender Systems for Family History Source Discovery.” 2017. Web. 09 Mar 2021.
Vancouver:
Brinton DJ. Recommender Systems for Family History Source Discovery. [Internet] [Masters thesis]. Brigham Young University; 2017. [cited 2021 Mar 09].
Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=7606&context=etd.
Council of Science Editors:
Brinton DJ. Recommender Systems for Family History Source Discovery. [Masters Thesis]. Brigham Young University; 2017. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=7606&context=etd
Hong Kong University of Science and Technology
7.
Zhao, Pengfei CSE.
Novelty and diversity based recommendation systems.
Degree: 2016, Hong Kong University of Science and Technology
URL: http://repository.ust.hk/ir/Record/1783.1-100374
;
https://doi.org/10.14711/thesis-b1628146
;
http://repository.ust.hk/ir/bitstream/1783.1-100374/1/th_redirect.html
► Traditional recommendation systems aim at generating recommendations that are relevant to the user’s interest. Thus, they are called relevance-based recommendation systems (RBRSs). The major drawback…
(more)
▼ Traditional recommendation systems aim at generating recommendations that are relevant to the user’s interest. Thus, they are called relevance-based recommendation systems (RBRSs). The major drawback of this approach is that user soon becomes very familiar with the recommendations and loses interest in reading and exploring them. In other words, relevance-based recommendations cannot help users to expand their interest and keep the recommendations exciting to them. Discovery-oriented recommendation systems (DORSs) aim to solve this problem by introducing discover utilities (DUs) such as novelty and diversity to improve the attractiveness of the recommendations to the user. In this thesis, we investigate techniques for improving the effectiveness of DORSs. Since novelty and diversity are the most important and widely studied DUs, we focus on recommendation systems that aim to improve the novelty and diversity of the recommendations. We study two important aspects of DORSs, namely, novelty and diversity of the recommendations. Existing DORSs generate recommendations that are optimized to balance between the accuracy and DUs of the recommendations to make the recommendations relevant and yet interesting to the user. However, they disregard an important fact that different users’ appetites for DUs are different. For example, a curious user can accept highly novel and diversified recommendations but a conservative user tends to respond only to recommendations she is familiar with. Thus, we propose a framework for curiosity-based recommendation systems (CBRSs) which can produce recommendations with an amount of DUs personalized to fit an individual user’s curiosity level. As a result, the recommendations are neither too surprising nor too boring for a user because the recommendations are customized to fit her unique curiosity. In order to model and quantify human curiosity, we adopt the curiosity arousing model (CAM) developed in psychology research and propose a probabilistic curiosity model (PCM) to model the psychological model computationally. Extensive experiments have been performed to evaluate the performance of CBRS against the baselines using a music dataset from last.fm. The results show that compared to the baselines CBRS not only provides more personalized recommendations that adapt to the user’s curiosity level but also improves the recommendation accuracy. To improve the diversity of the recommendations, we propose a recommendation framework by the unification of two types of diversity, namely, intra-list and temporal diversity, of the recommendations. Traditional RBRSs recommend items which are very similar to the user’s interest. As a result, the recommended items are also very similar between each other, making the items in a recommendation list monotonous. We name this “intra-monotony problem” (IMP). Further, most existing recommendation systems make recommendations without considering what has been recommended before. Thus, they may make similar recommendations over and over again, making the recommended…
Subjects/Keywords: Recommender systems (Information filtering)
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhao, P. C. (2016). Novelty and diversity based recommendation systems. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-100374 ; https://doi.org/10.14711/thesis-b1628146 ; http://repository.ust.hk/ir/bitstream/1783.1-100374/1/th_redirect.html
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):
Zhao, Pengfei CSE. “Novelty and diversity based recommendation systems.” 2016. Thesis, Hong Kong University of Science and Technology. Accessed March 09, 2021.
http://repository.ust.hk/ir/Record/1783.1-100374 ; https://doi.org/10.14711/thesis-b1628146 ; http://repository.ust.hk/ir/bitstream/1783.1-100374/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Zhao, Pengfei CSE. “Novelty and diversity based recommendation systems.” 2016. Web. 09 Mar 2021.
Vancouver:
Zhao PC. Novelty and diversity based recommendation systems. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2016. [cited 2021 Mar 09].
Available from: http://repository.ust.hk/ir/Record/1783.1-100374 ; https://doi.org/10.14711/thesis-b1628146 ; http://repository.ust.hk/ir/bitstream/1783.1-100374/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Zhao PC. Novelty and diversity based recommendation systems. [Thesis]. Hong Kong University of Science and Technology; 2016. Available from: http://repository.ust.hk/ir/Record/1783.1-100374 ; https://doi.org/10.14711/thesis-b1628146 ; http://repository.ust.hk/ir/bitstream/1783.1-100374/1/th_redirect.html
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
ITESO – Universidad Jesuita de Guadalajara
8.
Hernández-Ortiz, Miguel A.
Webpage recommender system
.
Degree: 2016, ITESO – Universidad Jesuita de Guadalajara
URL: http://hdl.handle.net/11117/4161
► En este trabajo se hace una introducción al campo de estudio de los sistemas de recomendación. Se pretende aplicarlo para desarrollar un sistema recomendador para…
(more)
▼ En este trabajo se hace una introducción al campo de estudio de los sistemas de recomendación. Se pretende aplicarlo para desarrollar un sistema recomendador para la página web de una empresa que sea capaz de hacer recomendaciones personalizadas a un usuario con base en sus intereses y comportamiento previo en el sitio. Por otro lado, este sistema también puede ser utilizado como una herramienta de promoción de nuevos productos. Finalmente, se hace una comparación de los distintos algoritmos que se implementan con datos reales de la empresa y los resultados que dieron. In this work there is an introduction to the study field of recommender systems. It is intended to be applied to develop a recommender system for a company’s webpage which is capable of making personalized recommendations to a user based on his interests and previous behavior on the site. Which can also be used as a promotional tool of new products. Finally, we do a comparison of the different algorithms we implemented running with real data of the company and the results we obtained.
Subjects/Keywords: Sistemas de Recomendación;
Recommender Systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hernández-Ortiz, M. A. (2016). Webpage recommender system
. (Thesis). ITESO – Universidad Jesuita de Guadalajara. Retrieved from http://hdl.handle.net/11117/4161
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):
Hernández-Ortiz, Miguel A. “Webpage recommender system
.” 2016. Thesis, ITESO – Universidad Jesuita de Guadalajara. Accessed March 09, 2021.
http://hdl.handle.net/11117/4161.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Hernández-Ortiz, Miguel A. “Webpage recommender system
.” 2016. Web. 09 Mar 2021.
Vancouver:
Hernández-Ortiz MA. Webpage recommender system
. [Internet] [Thesis]. ITESO – Universidad Jesuita de Guadalajara; 2016. [cited 2021 Mar 09].
Available from: http://hdl.handle.net/11117/4161.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Hernández-Ortiz MA. Webpage recommender system
. [Thesis]. ITESO – Universidad Jesuita de Guadalajara; 2016. Available from: http://hdl.handle.net/11117/4161
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
9.
Wafula, J.B. (author).
Evaluation of Machine Learning Algorithms for Outlier Detection in Clustered Code Fragments.
Degree: 2015, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:2cc644d3-c95c-4331-8b07-445d491f697f
► Many software systems are designed to be long-lived due to the costs involved in developing new systems. Changes in these systems are inevitable due to…
(more)
▼ Many software systems are designed to be long-lived due to the costs involved in developing new systems. Changes in these systems are inevitable due to constant modifications in requirements that are necessitated by the constantly changing nature of the business environment or detection of faults.To adapt their software to all these changing requirements developers use several tools, i.e. recommendations systems. To create recommendation patterns, the recommendation system searches for groups of similar code changes in software archives using syntactical similarities. As this search is a heuristic approach the groups contain outliers that prevent the generation of many useful patterns. In this thesis, we device algorithms that work hand in hand with various classification algorithms to identify outliers in the initial groups generated by SIFE. The goal is to improve the final recommendations presented to the developer. For the improved classification we first use manifold learning algorithms to map our data to a three dimensional space. We then use the 3D coordinates of generalizable groups as our feature vectors and train group-specific, project-specific and global classifiers. With these classifiers we make changes to the initial groups. We evaluate the results of the changed groups with the Disruptor, Retrofit, Picasso, Flym, Android Chart and Android Universal Image Loader software repositories. Our approach results in an improvement of up to 36% in the repositories. Joint degree with Friedrich-Alexander-Universität Erlangen-Nürnberg.
Erasmus Mundus COSSE
Applied mathematics
Electrical Engineering, Mathematics and Computer Science
Advisors/Committee Members: Vuik, C. (mentor).
Subjects/Keywords: Machine Learning; Recommender Systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Wafula, J. B. (. (2015). Evaluation of Machine Learning Algorithms for Outlier Detection in Clustered Code Fragments. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:2cc644d3-c95c-4331-8b07-445d491f697f
Chicago Manual of Style (16th Edition):
Wafula, J B (author). “Evaluation of Machine Learning Algorithms for Outlier Detection in Clustered Code Fragments.” 2015. Masters Thesis, Delft University of Technology. Accessed March 09, 2021.
http://resolver.tudelft.nl/uuid:2cc644d3-c95c-4331-8b07-445d491f697f.
MLA Handbook (7th Edition):
Wafula, J B (author). “Evaluation of Machine Learning Algorithms for Outlier Detection in Clustered Code Fragments.” 2015. Web. 09 Mar 2021.
Vancouver:
Wafula JB(. Evaluation of Machine Learning Algorithms for Outlier Detection in Clustered Code Fragments. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2021 Mar 09].
Available from: http://resolver.tudelft.nl/uuid:2cc644d3-c95c-4331-8b07-445d491f697f.
Council of Science Editors:
Wafula JB(. Evaluation of Machine Learning Algorithms for Outlier Detection in Clustered Code Fragments. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:2cc644d3-c95c-4331-8b07-445d491f697f
Delft University of Technology
10.
Dritsas, Athanasios (author).
Deep visual genre-aware descriptors for movie recommendation.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:4c264cda-3aee-46ca-9f20-1586d177b49c
► In the last years, the popularity of video-on-demand services has been constantly increasing, especially for the young audiences who are more adept at using new…
(more)
▼ In the last years, the popularity of video-on-demand services has been constantly increasing, especially for the young audiences who are more adept at using new technologies. Through those platforms, the viewers have access to a huge volume of movies at any moment that makes the viewing decision for most of them a very challenging task. Recommender systems are employed by video-on-demand providers to address the former challenge. We propose a novel movie recommender system that filters movies based on the genre-related visual elements of their trailers. The proposed system utilizes a 3D pre-trained deep ConvNet to extract spatio-temporal deep features from the trailers which then are combined, through a Deep Bag of Segments (DBoS) pooling network, with the genre information of the movie to provide a single movie representation. The 3D deep visual genre-aware representation is exploited by a pure content-based filtering system to provide personalized recommendations to users. We conduct offline experiments with two datasets to evaluate the performance of our approach with respect to accuracy and beyond accuracy metrics. We also conduct an online experiment in a real-world streaming platform to evaluate the user perceived utility of the recommendations produced by a pure content-based recommender system using our proposed genre-aware movie descriptor against the same system using genre and visual 3D deep features. We conclude that a continuous genre representation, which reflects genre specific visual elements of the movie, provides interesting results in the content-based movie recommendation task. Exploring further its potential could bring important benefits to various tasks in the movie domain.
Computer Science
Advisors/Committee Members: Larson, Martha (mentor), Bozzon, Alessandro (graduation committee), Gutierrez Granada, Mateo (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: recommender systems; multimedia; deep learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Dritsas, A. (. (2019). Deep visual genre-aware descriptors for movie recommendation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:4c264cda-3aee-46ca-9f20-1586d177b49c
Chicago Manual of Style (16th Edition):
Dritsas, Athanasios (author). “Deep visual genre-aware descriptors for movie recommendation.” 2019. Masters Thesis, Delft University of Technology. Accessed March 09, 2021.
http://resolver.tudelft.nl/uuid:4c264cda-3aee-46ca-9f20-1586d177b49c.
MLA Handbook (7th Edition):
Dritsas, Athanasios (author). “Deep visual genre-aware descriptors for movie recommendation.” 2019. Web. 09 Mar 2021.
Vancouver:
Dritsas A(. Deep visual genre-aware descriptors for movie recommendation. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 09].
Available from: http://resolver.tudelft.nl/uuid:4c264cda-3aee-46ca-9f20-1586d177b49c.
Council of Science Editors:
Dritsas A(. Deep visual genre-aware descriptors for movie recommendation. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:4c264cda-3aee-46ca-9f20-1586d177b49c
University of Minnesota
11.
Ekstrand, Michael.
Towards Recommender Engineering: tools and experiments for identifying recommender differences.
Degree: PhD, 2014, University of Minnesota
URL: http://hdl.handle.net/11299/165307
► Since the introduction of their modern form 20 years ago, recommender systems have proven a valuable tool for help users manage information overload.Two decades of…
(more)
▼ Since the introduction of their modern form 20 years ago, recommender systems have proven a valuable tool for help users manage information overload.Two decades of research have produced many algorithms for computing recommendations, mechanisms for evaluating their effectiveness, and user interfaces and experiences to embody them.It has also been found that the outputs of different recommendation algorithms differ in user-perceptible ways that affect their suitability to different tasks and information needs.However, there has been little work to systematically map out the space of algorithms and the characteristics they exhibit that makes them more or less effective in different applications. As a result, developers of recommender systems must experiment, conducting basic science on each application and its users to determine the approach(es) that will meet their needs.This thesis presents our work towards \emph{recommender engineering}: the design of recommender systems from well-understood principles of user needs, domain properties, and algorithm behaviors.This will reduce the experimentation required for each new recommender application, allowing developers to design recommender systems that are likely to be effective for their particular application.To that end, we make four contributions: the LensKit toolkit for conducting experiments on a wide variety of recommender algorithms and data sets under different experimental conditions (offline experiments with diverse metrics, online user studies, and the ability to grow to support additional methodologies), along with new developments in object-oriented software configuration to support this toolkit;experiments on the configuration options of widely-used algorithms to provide guidance on tuning and configuring them; an offline experiment on the differences in the errors made by different algorithms; and a user study on the user-perceptible differences between lists of movie recommendations produced by three common recommender algorithms.Much research is needed to fully realize the vision of recommender engineering in the coming years; it is our hope that LensKit will prove a valuable foundation for much of this work, and our experiments represent a small piece of the kinds of studies that must be carried out, replicated, and validated to enable recommender systems to be engineered.
Subjects/Keywords: Human-computer interaction; Recommender systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ekstrand, M. (2014). Towards Recommender Engineering: tools and experiments for identifying recommender differences. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/165307
Chicago Manual of Style (16th Edition):
Ekstrand, Michael. “Towards Recommender Engineering: tools and experiments for identifying recommender differences.” 2014. Doctoral Dissertation, University of Minnesota. Accessed March 09, 2021.
http://hdl.handle.net/11299/165307.
MLA Handbook (7th Edition):
Ekstrand, Michael. “Towards Recommender Engineering: tools and experiments for identifying recommender differences.” 2014. Web. 09 Mar 2021.
Vancouver:
Ekstrand M. Towards Recommender Engineering: tools and experiments for identifying recommender differences. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2021 Mar 09].
Available from: http://hdl.handle.net/11299/165307.
Council of Science Editors:
Ekstrand M. Towards Recommender Engineering: tools and experiments for identifying recommender differences. [Doctoral Dissertation]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/165307
University of Minnesota
12.
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
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
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 09, 2021.
http://hdl.handle.net/11299/195398.
MLA Handbook (7th Edition):
Christakopoulou, Evangelia. “Improving the Quality of Top-N Recommendation.” 2018. Web. 09 Mar 2021.
Vancouver:
Christakopoulou E. Improving the Quality of Top-N Recommendation. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2021 Mar 09].
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
McGill University
13.
Garden, Matthew.
On the Use of Semantic Feedback in Recommender Systems.
Degree: MS, School of Computer Science, 2004, McGill University
URL: https://escholarship.mcgill.ca/downloads/rx913s18d.pdf
;
https://escholarship.mcgill.ca/concern/theses/m613n093t
► Cette thèse présente une nouvelle approche aux systèmes de recommender. Les systèmes de recommender précédents bases sur filtrer en collaboration sollicitent typiquement les réactions d'utilisateur…
(more)
▼ Cette thèse présente une nouvelle approche aux systèmes de recommender. Les systèmes de recommender précédents bases sur filtrer en collaboration sollicitent typiquement les réactions d'utilisateur sur les articles de domaine comme les classements généraux qui sont alors enregistre comme les valeurs numériques. Ce paradigme limite la richesse sémantique de l'interaction de l'utilisateur avec le système et la profondeur a que le système peut comprendre les préférences d'utilisateur. Nous proposons un nouveau système de recommender, Recommendz, qui permet l'utilisateur de commenter pas seulement de la qualité générale de l'article mais aussi de la quantité et la qualité des caractéristiques de l'article. Ceci permet à l'utilisateur de justifier son ou ses classements et permet au système de comparer des utilisateurs pas seulement dans la référence a la préférence générale, mais aussi de comparer les raisons qui ont cause les préférences. Nous avons développé une implémentation de notre approche, et par il a recueilli la base des données empiriques étendues basées sur les classements de film. Nous démontrons l'efficacité de notre approche, et décrirons les détails de l'implémentation.
This thesis presents a new approach to recommender systems. Previous recommender systems based on collaborative filtering typically solicit user feedback on domain items as overall ratings which are then recorded as numeric values. This paradigm limits the semantic richness of the user's interaction with the system and the depth to which the system can understand user preferences. We propose a new recommender system, Recommendz, which allows the user to comment not only about the overall quality of the item but also about the quantity and quality of features of the item. This allows the user to justify his or her ratings and allows the system to compare users not only with respect to overall preference, but also to compare the reasons behind those preferences. We have developed an implementation of our approach, and have collected extensive empirical data based on movie ratings. We demonstrate the effectiveness of our approach, and describe the details of the implementation.
Advisors/Committee Members: Dudek, Greg (Supervisor).
Subjects/Keywords: Recommender systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Garden, M. (2004). On the Use of Semantic Feedback in Recommender Systems. (Masters Thesis). McGill University. Retrieved from https://escholarship.mcgill.ca/downloads/rx913s18d.pdf ; https://escholarship.mcgill.ca/concern/theses/m613n093t
Chicago Manual of Style (16th Edition):
Garden, Matthew. “On the Use of Semantic Feedback in Recommender Systems.” 2004. Masters Thesis, McGill University. Accessed March 09, 2021.
https://escholarship.mcgill.ca/downloads/rx913s18d.pdf ; https://escholarship.mcgill.ca/concern/theses/m613n093t.
MLA Handbook (7th Edition):
Garden, Matthew. “On the Use of Semantic Feedback in Recommender Systems.” 2004. Web. 09 Mar 2021.
Vancouver:
Garden M. On the Use of Semantic Feedback in Recommender Systems. [Internet] [Masters thesis]. McGill University; 2004. [cited 2021 Mar 09].
Available from: https://escholarship.mcgill.ca/downloads/rx913s18d.pdf ; https://escholarship.mcgill.ca/concern/theses/m613n093t.
Council of Science Editors:
Garden M. On the Use of Semantic Feedback in Recommender Systems. [Masters Thesis]. McGill University; 2004. Available from: https://escholarship.mcgill.ca/downloads/rx913s18d.pdf ; https://escholarship.mcgill.ca/concern/theses/m613n093t
University of New South Wales
14.
Zhou, Bowen.
Advanced Collaborative Filtering and Image-based Recommender Systems.
Degree: Computer Science & Engineering, 2017, University of New South Wales
URL: http://handle.unsw.edu.au/1959.4/60049
;
https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51367/SOURCE2?view=true
► Due to burst of growth of information available all over the world, it has been of great necessity to retrieve most suitable data from the…
(more)
▼ Due to burst of growth of information available all over the world, it has been of great necessity to retrieve most suitable data from the entire warehouse for each unique person. E-commerce can be considered as an example which possess vast amount of products that need to be personalized and then recommended to customers. In this case, Recommender Systems (RS) have been frequently studied in recent years. In this thesis, we firstly focus on improving performance of Collaborative Filtering (CF) and then develop new approaches to alleviate the cold-start problem.We firstly improve the SGD-base Matrix Factorization method, which is one of the most effective CF approaches, by taking into consideration item attributes. The developed MFA model treat categorical information of items as virtual users and then insert ratings of maximum value into the original rating matrix if an item belongs to the category. Then k-nearest-neighbour (KNN) method is combined with MFA model to make further improvement. A threshold is then set during SGD to filter out the most misleading ratings before the SGD is applied again to train the factors.The second section of our research is to use other side information with user's past purchase record to generate future recommendations. At first, helpfulness information (i.e. helpful votes provided by other members in the community) of each review is used to produce the Weighted Matrix Factorization (WMF) model. Then timestamp of each rating is substituted into calculation of factors. In this section, WMF and TWMF are implemented on chronologically sorted datasets, so that it could simulate more real situation.In the last section, we assume that all products to be recommended are completely new to the system, meaning that none of their information such as colour, price, categories can be substituted into calculation. Therefore, classic CF models will fail to learn factors of these new products. In this case, we develop image-based RS to recommend visually similar products, in which SSIM/CW-SSIM, CNN with KNN, CNN with ridge regression and CNN with SGD are used to achieve the target, and all of them are proved to be effective.
Subjects/Keywords: Collaborative Filtering; Recommender Systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhou, B. (2017). Advanced Collaborative Filtering and Image-based Recommender Systems. (Masters Thesis). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60049 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51367/SOURCE2?view=true
Chicago Manual of Style (16th Edition):
Zhou, Bowen. “Advanced Collaborative Filtering and Image-based Recommender Systems.” 2017. Masters Thesis, University of New South Wales. Accessed March 09, 2021.
http://handle.unsw.edu.au/1959.4/60049 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51367/SOURCE2?view=true.
MLA Handbook (7th Edition):
Zhou, Bowen. “Advanced Collaborative Filtering and Image-based Recommender Systems.” 2017. Web. 09 Mar 2021.
Vancouver:
Zhou B. Advanced Collaborative Filtering and Image-based Recommender Systems. [Internet] [Masters thesis]. University of New South Wales; 2017. [cited 2021 Mar 09].
Available from: http://handle.unsw.edu.au/1959.4/60049 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51367/SOURCE2?view=true.
Council of Science Editors:
Zhou B. Advanced Collaborative Filtering and Image-based Recommender Systems. [Masters Thesis]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/60049 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51367/SOURCE2?view=true
University of Johannesburg
15.
Leonard, Justin Sean.
PerTrust : leveraging personality and trust for group recommendations.
Degree: 2014, University of Johannesburg
URL: http://hdl.handle.net/10210/11364
► M.Sc. (Information Technology)
Recommender systems assist a system user to identify relevant content within a specific context. This is typically performed through an analysis of…
(more)
▼ M.Sc. (Information Technology)
Recommender systems assist a system user to identify relevant content within a specific context. This is typically performed through an analysis of a system user’s rating habits and personal preferences and leveraging these to return one or a number of relevant recommendations. There are numerable contexts in which recommender systems can be applied, such as movies, tourism, books, and music. The need for recommender systems has become increasingly relevant, particularly on the Internet. This is mainly due to the exponential amount of content that is published online on a daily basis. It has thus become more time consuming and difficult to find pertinent information online, leading to information overload. The relevance of a recommender system, therefore, is to assist a system user to overcome the information overload problem by identifying pertinent information on their behalf. There has been much research done within the recommender system field and how such systems can best recommend items to an individual user. However, a growing and more recent research area is how recommender systems can be extended to recommend items to groups, known as group recommendation. The relevance of group recommendation is that many contexts of recommendation apply to both individuals and groups. For example, people often watch movies or visit tourist attractions as part of a group. Group recommendation is an inherently more complex form of recommendation than individual recommendation for a number of reasons. The first reason is that the rating habits and personal preferences of each system user within the group need to be considered. Additionally, these rating habits and personal preferences can be quite heterogeneous in nature. Therefore, group recommendation becomes complex because a satisfactory recommendation needs to be one which meets the preferences of each group member and not just a single group member. The second reason why group recommendation is considered to be more complex than individual recommendation is because a group not only includes multiple personal preferences, but also multiple personality types. This means that a group is more complex from a social perspective. Therefore, a satisfactory group recommendation needs to be one which considers the varying personality types and behaviours of the group. The purpose of this research is to present PerTrust, a generic framework for group recommendation with the purpose of providing a possible solution to the aforementioned issues noted above. The primary focus of PerTrust is how to leverage both personality and trust in overcoming these issues.
Subjects/Keywords: Recommender systems (Information filtering); Information filtering systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Leonard, J. S. (2014). PerTrust : leveraging personality and trust for group recommendations. (Thesis). University of Johannesburg. Retrieved from http://hdl.handle.net/10210/11364
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):
Leonard, Justin Sean. “PerTrust : leveraging personality and trust for group recommendations.” 2014. Thesis, University of Johannesburg. Accessed March 09, 2021.
http://hdl.handle.net/10210/11364.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Leonard, Justin Sean. “PerTrust : leveraging personality and trust for group recommendations.” 2014. Web. 09 Mar 2021.
Vancouver:
Leonard JS. PerTrust : leveraging personality and trust for group recommendations. [Internet] [Thesis]. University of Johannesburg; 2014. [cited 2021 Mar 09].
Available from: http://hdl.handle.net/10210/11364.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Leonard JS. PerTrust : leveraging personality and trust for group recommendations. [Thesis]. University of Johannesburg; 2014. Available from: http://hdl.handle.net/10210/11364
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Minnesota
16.
Narayan, Anshuman.
What is the Value of Rating Obscure Items? An Analysis of the Effect of Less-Popular Items on Recommendation Quality.
Degree: MS, Computer Science, 2019, University of Minnesota
URL: http://hdl.handle.net/11299/206148
► Recommender systems designers believe that the system stands to benefit from the users rating items that do not have many ratings. However, the effect of…
(more)
▼ Recommender systems designers believe that the system stands to benefit from the users rating items that do not have many ratings. However, the effect of this act of rating lesser known items on the user’s recommendations is unknown. This leads to asking the question of whether these low popularity items affect the recommendations received by users. This work looks at the effect less popular items have on a user’s recommendations and the prediction and recommendations metrics that quantify the quality of recommendations. Using a matrix factorization model to build a recommender system, we modify a subset of users’ ratings data and look at the difference in recommendations generated. We also make use of popular recommender systems metrics such as nDCG, Precison and Recall to evaluate the effect of these modifications.Apart from looking at the ef- fect of this ”truncation” of casual user ratings data on the casual users themselves, we also look at the effects of this ”truncation” on the more invested users of the system, in terms of top-n recommendation and prediction metrics. The results of these evalu- ations appear promising, with very little to no loss of information, personalization or metric scores for more casual users. The results of these evaluations for more serious users also appears to have little effect on the performance of top-n recommendation and prediction metrics.
Subjects/Keywords: Matrix Factorization techniques; New user; Recommender System design; Recommender Systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Narayan, A. (2019). What is the Value of Rating Obscure Items? An Analysis of the Effect of Less-Popular Items on Recommendation Quality. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206148
Chicago Manual of Style (16th Edition):
Narayan, Anshuman. “What is the Value of Rating Obscure Items? An Analysis of the Effect of Less-Popular Items on Recommendation Quality.” 2019. Masters Thesis, University of Minnesota. Accessed March 09, 2021.
http://hdl.handle.net/11299/206148.
MLA Handbook (7th Edition):
Narayan, Anshuman. “What is the Value of Rating Obscure Items? An Analysis of the Effect of Less-Popular Items on Recommendation Quality.” 2019. Web. 09 Mar 2021.
Vancouver:
Narayan A. What is the Value of Rating Obscure Items? An Analysis of the Effect of Less-Popular Items on Recommendation Quality. [Internet] [Masters thesis]. University of Minnesota; 2019. [cited 2021 Mar 09].
Available from: http://hdl.handle.net/11299/206148.
Council of Science Editors:
Narayan A. What is the Value of Rating Obscure Items? An Analysis of the Effect of Less-Popular Items on Recommendation Quality. [Masters Thesis]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206148
Texas State University – San Marcos
17.
Mahant, Vaibhav.
Improving Top-N Evaluation of Recommender Systems.
Degree: MS, Computer Science, 2016, Texas State University – San Marcos
URL: https://digital.library.txstate.edu/handle/10877/6309
► Recommender systems are used to provide the user with a list of recommended items to help user find new items they might prefer. One of…
(more)
▼ Recommender systems are used to provide the user with a list of recommended items to help user find new items they might prefer. One of the main task of the
recommender is to provide such items that the user has not seen before. But while evaluating, if the
recommender correctly predicts such items we penalize the
recommender, usually because the relevance of the item for that user is unknown, and because of the unknown relevance the item being recommended was not present in the test set of the
recommender. In
recommender systems it is very hard to get the relevance of every item for every user. In this research we are trying to address this problem by randomly adding decoys into the recommender’s test set. We will be measuring the performance of the
recommender with different decoy sizes. We find that random decoys are exaggerating the advantage of popular-item recommenders, casting doubt on their usefulness.
Advisors/Committee Members: Ekstrand, Michael (advisor), Gao, Byron (committee member), Metsis, Vangelis (committee member).
Subjects/Keywords: Recommender Systems; Evaluation; Recommender systems (Information filtering); Management information systems; Artificial intelligence – Data processing
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Mahant, V. (2016). Improving Top-N Evaluation of Recommender Systems. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6309
Chicago Manual of Style (16th Edition):
Mahant, Vaibhav. “Improving Top-N Evaluation of Recommender Systems.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed March 09, 2021.
https://digital.library.txstate.edu/handle/10877/6309.
MLA Handbook (7th Edition):
Mahant, Vaibhav. “Improving Top-N Evaluation of Recommender Systems.” 2016. Web. 09 Mar 2021.
Vancouver:
Mahant V. Improving Top-N Evaluation of Recommender Systems. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2021 Mar 09].
Available from: https://digital.library.txstate.edu/handle/10877/6309.
Council of Science Editors:
Mahant V. Improving Top-N Evaluation of Recommender Systems. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6309
University of California – Irvine
18.
Seaver, Nicholas Patrick.
Computing Taste: The Making of Algorithmic Music Recommendation.
Degree: Anthropology, 2015, University of California – Irvine
URL: http://www.escholarship.org/uc/item/1p64m732
► This dissertation reports on several years of multi-sited ethnographic fieldwork with the developers of algorithmic music recommendation systems in the US. It identifies and contributes…
(more)
▼ This dissertation reports on several years of multi-sited ethnographic fieldwork with the developers of algorithmic music recommendation systems in the US. It identifies and contributes to a nascent, transdisciplinary body of scholarship in “critical algorithm studies”—studies of algorithms’ sociocultural lives by scholars outside of mathematics or computer science. It argues that critics should concern themselves not with “algorithms” narrowly defined, but with sociotechnical “algorithmic systems,” of which humans are an integral part. It proposes that ethnography is a useful method for apprehending the cultural features of algorithmic systems and that these cultural features play a crucial role in the functioning of algorithms and how they change over time. Recommender systems provide a case in which to investigate these cultural concerns as they play out in the development of “preferential technics”—the intermingling of circulatory infrastructures with theories about taste. Arguing that theories of taste are embedded in algorithmic systems, the dissertation examines three areas that demonstrate this intermingling: listeners, music, and listening. The chapter on listeners describes how recommender systems have come to be used as tools for capturing users, bringing the anthropological literature on trapping to bear on the question of how imagined listeners inform the design of systems for captivating them. The chapter on music investigates how developers imagine music to occupy a “similarity space,” through which recommenders help listeners travel; theories about the nature of that space and the influence of developers on it mediate between understandings of space as a constructed or as a discovered order. The chapter on listening examines the changing techniques through which computers are taught to “hear” musical sound, arguing that the quantification of music is not simply a rationalization, but the establishment of a resonance between auditory and quantitative phenomena with unanticipated consequences. The conclusion explores the similarity between ethnographic methods and big data analytics, understood through the frame of “attention.” Thinking of algorithmic systems and critical research methods as techniques for organizing attention offers new, fruitful avenues for critical algorithm studies.
Subjects/Keywords: Cultural anthropology; algorithms; music; recommender systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Seaver, N. P. (2015). Computing Taste: The Making of Algorithmic Music Recommendation. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/1p64m732
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):
Seaver, Nicholas Patrick. “Computing Taste: The Making of Algorithmic Music Recommendation.” 2015. Thesis, University of California – Irvine. Accessed March 09, 2021.
http://www.escholarship.org/uc/item/1p64m732.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Seaver, Nicholas Patrick. “Computing Taste: The Making of Algorithmic Music Recommendation.” 2015. Web. 09 Mar 2021.
Vancouver:
Seaver NP. Computing Taste: The Making of Algorithmic Music Recommendation. [Internet] [Thesis]. University of California – Irvine; 2015. [cited 2021 Mar 09].
Available from: http://www.escholarship.org/uc/item/1p64m732.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Seaver NP. Computing Taste: The Making of Algorithmic Music Recommendation. [Thesis]. University of California – Irvine; 2015. Available from: http://www.escholarship.org/uc/item/1p64m732
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Ryerson University
19.
Sivapalan, Sanjeevan.
A genetic algorithm approach to recommender system cold start problem.
Degree: 2015, Ryerson University
URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A3664
► Recommender systems (RS) are ubiquitous and used in many systems to augment user experience to improve usability and they achieve this by helping users discover…
(more)
▼ Recommender systems (RS) are ubiquitous and used in many
systems to augment user experience to improve
usability and they achieve this by helping users discover new products to consume. They, however, suffer from cold-start problem which occurs when there is not enough information to generate recommendations to a user. Cold-start occurs when a new user enters the system that we don’t know about. We have proposed a novel algorithm to make recommendations to new users by recommending outside of their preferences. We also propose a genetic algorithm based solution to make recommendations when we lack information about user and a transitive algorithm to form neighbourhood. Altogether, we developed three algorithms and tested them using they MovieLens dataset. We have found that all of our algorithms performed well during our testing using the offline-evaluation method.
Advisors/Committee Members: Sadeghian, Alireza (Thesis advisor), Ryerson University (Degree grantor).
Subjects/Keywords: Recommender systems (Information filtering); Genetic algorithms.
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sivapalan, S. (2015). A genetic algorithm approach to recommender system cold start problem. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A3664
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):
Sivapalan, Sanjeevan. “A genetic algorithm approach to recommender system cold start problem.” 2015. Thesis, Ryerson University. Accessed March 09, 2021.
https://digital.library.ryerson.ca/islandora/object/RULA%3A3664.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sivapalan, Sanjeevan. “A genetic algorithm approach to recommender system cold start problem.” 2015. Web. 09 Mar 2021.
Vancouver:
Sivapalan S. A genetic algorithm approach to recommender system cold start problem. [Internet] [Thesis]. Ryerson University; 2015. [cited 2021 Mar 09].
Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A3664.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sivapalan S. A genetic algorithm approach to recommender system cold start problem. [Thesis]. Ryerson University; 2015. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A3664
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Ryerson University
20.
Islam, Masudul.
Personalized recommender system on whom to follow in Twitter.
Degree: 2014, Ryerson University
URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A2958
► Recommender systems have been widely used in social networking sites. In this thesis, we propose a novel approach to recommend new followees to Twitter users…
(more)
▼ Recommender systems have been widely used in social networking sites. In this thesis, we propose a novel approach to recommend new followees to Twitter users by learning their historic friends-adding patterns. Based on a user’s past social graph and her interactions with other connected users, scores based on some of the commonly used recommendation strategies are calculated and passed into the learning machine along with the recently added list of followees of the user. Learning to rank algorithm then identifies the best combination of recommendation strategies the user adopted to add new followees in the past. Although users may not adopt any recommendation strategies explicitly, they may subconsciously or implicitly use some. If the actually added followees match with the ones suggested by the recommendation strategy, we consider users are implicitly using that strategy. The experiment using the real data collected from Twitter proves the effectiveness of the proposed approach.
Advisors/Committee Members: Ryerson University (Degree grantor).
Subjects/Keywords: Recommender systems (Information filtering); Online social networks
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Islam, M. (2014). Personalized recommender system on whom to follow in Twitter. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A2958
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):
Islam, Masudul. “Personalized recommender system on whom to follow in Twitter.” 2014. Thesis, Ryerson University. Accessed March 09, 2021.
https://digital.library.ryerson.ca/islandora/object/RULA%3A2958.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Islam, Masudul. “Personalized recommender system on whom to follow in Twitter.” 2014. Web. 09 Mar 2021.
Vancouver:
Islam M. Personalized recommender system on whom to follow in Twitter. [Internet] [Thesis]. Ryerson University; 2014. [cited 2021 Mar 09].
Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A2958.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Islam M. Personalized recommender system on whom to follow in Twitter. [Thesis]. Ryerson University; 2014. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A2958
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Temple University
21.
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;
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
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 09, 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. 09 Mar 2021.
Vancouver:
Zhao F. Learning Top-N Recommender Systems with Implicit Feedbacks. [Internet] [Doctoral dissertation]. Temple University; 2017. [cited 2021 Mar 09].
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
Texas A&M University
22.
Jayarathna, Ukwatta Kankanamalage Sampath.
Unified Implicit and Explicit Feedback for Multi-Application User Interest Modeling.
Degree: PhD, Computer Science, 2016, Texas A&M University
URL: http://hdl.handle.net/1969.1/158044
► A user often interacts with multiple applications while working on a task. User models can be developed individually at each of the individual applications, but…
(more)
▼ A user often interacts with multiple applications while working on a task. User models can be developed individually at each of the individual applications, but there is no easy way to come up with a more complete user model based on the distributed activity of the user. To address this issue, this research studies the importance of combining various implicit and explicit relevance feedback indicators in a multi-application environment. It allows different applications used for different purposes by the user to contribute user activity and its context to mutually support users with unified relevance feedback. Using the data collected by the web browser, Microsoft Word and Microsoft PowerPoint, Adobe Acrobat Writer and VKB, combinations of implicit relevance feedback with semi-explicit relevance feedback were analyzed and compared with explicit user ratings.
Our past research show that multi-application interest models based on implicit feedback theoretically out performed single application interest models based on implicit feedback. Also in practice, a multi-application interest model based on semi-explicit feedback increased user attention to high-value documents. In the current dissertation study, we have incorporated topic modeling to represent interest in user models for textual content and compared similarity measures for improved recall and precision based on the text content. We also learned the relative value of features from content consumption applications and content production applications. Our experimental results show that incorporating implicit feedback in page-level user interest estimation resulted in significant improvements over the baseline models. Furthermore, incorporating semi-explicit content (e.g. annotated text) with the authored text is effective in identifying segment-level relevant content.
We have evaluated the effectiveness of the recommendation support from both semi-explicit model (authored/annotated text) and unified model (implicit + semi-explicit) and have found that they are successful in allowing users to locate the content easily because the relevant details are selectively highlighted and recommended documents and passages within documents based on the user’s indicated interest. Our recommendations based on the semi-explicit feedback were viewed the same as those from unified feedback and recommendations based on semi-explicit feedback outperformed those from unified feedback in terms of matching post-task document assessments.
Advisors/Committee Members: Shipman, Frank (advisor), Furuta, Richard (committee member), Caverlee, James (committee member), McNamara, Ann (committee member).
Subjects/Keywords: user interest modeling; relevance feedback; recommender systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jayarathna, U. K. S. (2016). Unified Implicit and Explicit Feedback for Multi-Application User Interest Modeling. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/158044
Chicago Manual of Style (16th Edition):
Jayarathna, Ukwatta Kankanamalage Sampath. “Unified Implicit and Explicit Feedback for Multi-Application User Interest Modeling.” 2016. Doctoral Dissertation, Texas A&M University. Accessed March 09, 2021.
http://hdl.handle.net/1969.1/158044.
MLA Handbook (7th Edition):
Jayarathna, Ukwatta Kankanamalage Sampath. “Unified Implicit and Explicit Feedback for Multi-Application User Interest Modeling.” 2016. Web. 09 Mar 2021.
Vancouver:
Jayarathna UKS. Unified Implicit and Explicit Feedback for Multi-Application User Interest Modeling. [Internet] [Doctoral dissertation]. Texas A&M University; 2016. [cited 2021 Mar 09].
Available from: http://hdl.handle.net/1969.1/158044.
Council of Science Editors:
Jayarathna UKS. Unified Implicit and Explicit Feedback for Multi-Application User Interest Modeling. [Doctoral Dissertation]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/158044
University of Toronto
23.
Russell, Travis.
An MDP-based Coupon Issuing System.
Degree: 2015, University of Toronto
URL: http://hdl.handle.net/1807/70564
► We present a system based on the work of Shani et al. [An MDP-based recommender system," Journal of Machine Learning Research, vol. 6, pp. 1265-1295,…
(more)
▼ We present a system based on the work of Shani et al. [An MDP-based recommender system," Journal of Machine Learning Research, vol. 6, pp. 1265-1295, 2005], who showed that the recommendation process could be modeled as a sequential decision process and that a Markov decision process (MDP) provided an adequate representation of the process. The major addition to our system is that of coupons. Given a set of coupons, and an integer n > 0, our system will issue users a coupon every n purchases. This system determines the optimal coupon to issue each user by analyzing their purchase history and the potential profit made from issuing the coupon. We also present an additional method for determining transition probabilities, a method for updating the system in real-time, and a method for solving our system that is potentially more computationally efficient.
M.Sc.
Advisors/Committee Members: Murty, Kumar, Mathematics.
Subjects/Keywords: machine learning; probabilistic models; recommender systems; 0405
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Russell, T. (2015). An MDP-based Coupon Issuing System. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/70564
Chicago Manual of Style (16th Edition):
Russell, Travis. “An MDP-based Coupon Issuing System.” 2015. Masters Thesis, University of Toronto. Accessed March 09, 2021.
http://hdl.handle.net/1807/70564.
MLA Handbook (7th Edition):
Russell, Travis. “An MDP-based Coupon Issuing System.” 2015. Web. 09 Mar 2021.
Vancouver:
Russell T. An MDP-based Coupon Issuing System. [Internet] [Masters thesis]. University of Toronto; 2015. [cited 2021 Mar 09].
Available from: http://hdl.handle.net/1807/70564.
Council of Science Editors:
Russell T. An MDP-based Coupon Issuing System. [Masters Thesis]. University of Toronto; 2015. Available from: http://hdl.handle.net/1807/70564
24.
Soomro, Kamran.
HyDRA hybrid workflow design recommender architecture.
Degree: PhD, 2016, University of the West of England, Bristol
URL: https://uwe-repository.worktribe.com/output/911414
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680875
► Workflows are a way to describe a series of computations on raw e-Science data. These data may be MRI brain scans, data from a high…
(more)
▼ Workflows are a way to describe a series of computations on raw e-Science data. These data may be MRI brain scans, data from a high energy physics detector or metric data from an earth observation project. In order to derive meaningful knowledge from the data, it must be processed and analysed. Workflows have emerged as the principle mechanism for describing and enacting complex e-Science analyses on distributed infrastructures such as grids. Scientific users face a number of challenges when designing workflows. These challenges include selecting appropriate components for their tasks, spec- ifying dependencies between them and selecting appropriate parameter values. These tasks become especially challenging as workflows become increasingly large. For example, the CIVET workflow consists of up to 108 components. Building the workflow by hand and specifying all the links can become quite cumbersome for scientific users. Traditionally, recommender systems have been employed to assist users in such time-consuming and tedious tasks. One of the techniques used by recommender systems has been to predict what the user is attempting to do using a variety of techniques. These techniques include using workflow se- mantics on the one hand and historical usage patterns on the other. Semantics-based systems attempt to infer a user’s intentions based on the available semantics. Pattern-based systems attempt to extract usage patterns from previously-constructed workflows and match those patterns to the workflow un- der construction. The use of historical patterns adds dynamism to the suggestions as the system can learn and adapt with “experience”. However, in cases where there are no previous patterns to draw upon, pattern-based systems fail to perform. Semantics-based systems, on the other hand infer from static information, so they always have something to draw upon. However, that information first has to be encoded into the semantic repository for the system to draw upon it, which is a time-consuming and tedious task in it self. Moreover, semantics-based systems do not learn and adapt with experience. Both approaches have distinct, but complementary features and drawbacks. By combining the two approaches, the drawbacks of each approach can be addressed. This thesis presents HyDRA, a novel hybrid framework that combines frequent usage patterns and workflow semantics to generate suggestions. The functions performed by the framework include; a) extracting frequent functional usage patterns; b) identifying the semantics of unknown components; and c) generating accurate and meaningful suggestions. Challenges to mining frequent patterns in- clude ensuring that meaningful and useful patterns are extracted. For this purpose only patterns that occur above a minimum frequency threshold are mined. Moreover, instead of just groups of specific components, the pattern mining algorithm takes into account workflow component semantics. This allows the system to identify different types of components that perform a single composite function. One…
Subjects/Keywords: 006.3; HyDRA; recommender systems; semantics; workflows
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Soomro, K. (2016). HyDRA hybrid workflow design recommender architecture. (Doctoral Dissertation). University of the West of England, Bristol. Retrieved from https://uwe-repository.worktribe.com/output/911414 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680875
Chicago Manual of Style (16th Edition):
Soomro, Kamran. “HyDRA hybrid workflow design recommender architecture.” 2016. Doctoral Dissertation, University of the West of England, Bristol. Accessed March 09, 2021.
https://uwe-repository.worktribe.com/output/911414 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680875.
MLA Handbook (7th Edition):
Soomro, Kamran. “HyDRA hybrid workflow design recommender architecture.” 2016. Web. 09 Mar 2021.
Vancouver:
Soomro K. HyDRA hybrid workflow design recommender architecture. [Internet] [Doctoral dissertation]. University of the West of England, Bristol; 2016. [cited 2021 Mar 09].
Available from: https://uwe-repository.worktribe.com/output/911414 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680875.
Council of Science Editors:
Soomro K. HyDRA hybrid workflow design recommender architecture. [Doctoral Dissertation]. University of the West of England, Bristol; 2016. Available from: https://uwe-repository.worktribe.com/output/911414 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680875
Tampere University
25.
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
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
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 09, 2021.
https://trepo.tuni.fi/handle/10024/105736.
MLA Handbook (7th Edition):
Machado, Lucas. “Fair team recommendations for multidisciplinary projects
.” 2019. Web. 09 Mar 2021.
Vancouver:
Machado L. Fair team recommendations for multidisciplinary projects
. [Internet] [Masters thesis]. Tampere University; 2019. [cited 2021 Mar 09].
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
Hong Kong University of Science and Technology
26.
Chen, Tianwen CSE.
Session-based recommendation with local invariance.
Degree: 2019, Hong Kong University of Science and Technology
URL: http://repository.ust.hk/ir/Record/1783.1-102332
;
https://doi.org/10.14711/thesis-991012757569403412
;
http://repository.ust.hk/ir/bitstream/1783.1-102332/1/th_redirect.html
► Session-based recommendation is a task to predict users’ next actions given a sequence of previous actions in the same session. Existing methods either encode the…
(more)
▼ Session-based recommendation is a task to predict users’ next actions given a sequence of previous actions in the same session. Existing methods either encode the previous actions in a strict order or completely ignore the order. It is not necessary to always capture the sequential information in sessions by following a strict order, because sometimes, the order of actions in a short sub-sequence, called the detailed order, may not be important, e.g., when a user is just comparing the same kind of products from different brands. We term the property that the order of actions in the sub-session level does not matter the local invariance. Nevertheless, the high-level ordering information is still useful because the data is sequential in nature. Therefore, a good session-based recommender should consider the local invariance property while capturing the sequential information by paying different attention to the ordering information in different levels of granularity. To this end, we propose a novel model called LINet to automatically ignore the insignificant detailed ordering information in some sub-sessions, while keeping the high-level sequential information of the whole sessions. In the model, we first use a full self-attention layer with Gaussian weighting to extract features of sub-sessions, and then we apply a recurrent neural network to capture the high-level sequential information. Extensive experiments on two real-world datasets show that our method outperforms or matches the state-of-the-art methods and the proposed mechanism to consider the local invariance property plays an important role.
Subjects/Keywords: Recommender systems (Information filtering)
; Mathematical models
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Chen, T. C. (2019). Session-based recommendation with local invariance. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-102332 ; https://doi.org/10.14711/thesis-991012757569403412 ; http://repository.ust.hk/ir/bitstream/1783.1-102332/1/th_redirect.html
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, Tianwen CSE. “Session-based recommendation with local invariance.” 2019. Thesis, Hong Kong University of Science and Technology. Accessed March 09, 2021.
http://repository.ust.hk/ir/Record/1783.1-102332 ; https://doi.org/10.14711/thesis-991012757569403412 ; http://repository.ust.hk/ir/bitstream/1783.1-102332/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Chen, Tianwen CSE. “Session-based recommendation with local invariance.” 2019. Web. 09 Mar 2021.
Vancouver:
Chen TC. Session-based recommendation with local invariance. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2019. [cited 2021 Mar 09].
Available from: http://repository.ust.hk/ir/Record/1783.1-102332 ; https://doi.org/10.14711/thesis-991012757569403412 ; http://repository.ust.hk/ir/bitstream/1783.1-102332/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Chen TC. Session-based recommendation with local invariance. [Thesis]. Hong Kong University of Science and Technology; 2019. Available from: http://repository.ust.hk/ir/Record/1783.1-102332 ; https://doi.org/10.14711/thesis-991012757569403412 ; http://repository.ust.hk/ir/bitstream/1783.1-102332/1/th_redirect.html
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Edinburgh
27.
Givon, Sharon.
Predicting and using social tags to improve the accuracy and transparency of recommender systems.
Degree: PhD, 2011, University of Edinburgh
URL: http://hdl.handle.net/1842/5770
► This thesis describes work on using content to improve recommendation systems. Personalised recommendations help potential buyers filter information and identify products that they might be…
(more)
▼ This thesis describes work on using content to improve recommendation systems. Personalised recommendations help potential buyers filter information and identify products that they might be interested in. Current recommender systems are based mainly on collaborative filtering (CF) methods, which suffer from two main problems: (1) the ramp-up problem, where items that do not have a sufficient amount of meta-data associated with them cannot be recommended; and (2) lack of transparency due to the fact that recommendations produced by the system are not clearly explained. In this thesis we tackle both of these problems. We outline a framework for generating more accurate recommendations that are based solely on available textual content or in combination with rating information. In particular, we show how content in the form of social tags can help improve recommendations in the book and movie domains. We address the ramp-up problem and show how in cases where they do not exist, social tags can be automatically predicted from available textual content, such as the full texts of books. We evaluate our methods using two sets of data that differ in product type and size. Finally we show how once products are selected to be recommended, social tags can be used to explain the recommendations. We conduct a web-based study to evaluate different styles of explanations and demonstrate how tag-based explanations outperform a common CF-based explanation and how a textual review-like explanation yields the best results in helping users predict how much they will like the recommended items.
Subjects/Keywords: 300.285; recommender systems; social tags; textual content
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Givon, S. (2011). Predicting and using social tags to improve the accuracy and transparency of recommender systems. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/5770
Chicago Manual of Style (16th Edition):
Givon, Sharon. “Predicting and using social tags to improve the accuracy and transparency of recommender systems.” 2011. Doctoral Dissertation, University of Edinburgh. Accessed March 09, 2021.
http://hdl.handle.net/1842/5770.
MLA Handbook (7th Edition):
Givon, Sharon. “Predicting and using social tags to improve the accuracy and transparency of recommender systems.” 2011. Web. 09 Mar 2021.
Vancouver:
Givon S. Predicting and using social tags to improve the accuracy and transparency of recommender systems. [Internet] [Doctoral dissertation]. University of Edinburgh; 2011. [cited 2021 Mar 09].
Available from: http://hdl.handle.net/1842/5770.
Council of Science Editors:
Givon S. Predicting and using social tags to improve the accuracy and transparency of recommender systems. [Doctoral Dissertation]. University of Edinburgh; 2011. Available from: http://hdl.handle.net/1842/5770
Hong Kong University of Science and Technology
28.
Lu, Zhongqi.
Selective transfer learning for cross domain recommendation.
Degree: 2013, Hong Kong University of Science and Technology
URL: http://repository.ust.hk/ir/Record/1783.1-7976
;
https://doi.org/10.14711/thesis-b1240240
;
http://repository.ust.hk/ir/bitstream/1783.1-7976/1/th_redirect.html
► Collaborative Filtering (CF) aims to predict users’ ratings on items according to historical user-itempreference data. In many real-world applications, preference data are usually sparse, which…
(more)
▼ Collaborative Filtering (CF) aims to predict users’ ratings on items according to historical user-itempreference data. In many real-world applications, preference data are usually sparse, which would make models overfit and fail to give accurate predictions. Recently, several research works show that by transferring knowledge from some manually selected source domains, the data sparseness problem could be mitigated. However for most cases, parts of the source domain data are not consistent with the observations in the target domain, which may misguide the target domain model building. In this paper, we propose a novel criterion based on empirical prediction error and its variance to capture the consistency across domains in CF settings. Consequently, we embed this criterion into a boosting framework to perform selective knowledge transfer. Comparing with several state-of-the-art methods, we show that our proposed selective transfer learning framework can significantly improve the accuracy of rating prediction on several real-world recommendation tasks.
Subjects/Keywords: Recommender systems (Information filtering)
; Machine learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lu, Z. (2013). Selective transfer learning for cross domain recommendation. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-7976 ; https://doi.org/10.14711/thesis-b1240240 ; http://repository.ust.hk/ir/bitstream/1783.1-7976/1/th_redirect.html
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):
Lu, Zhongqi. “Selective transfer learning for cross domain recommendation.” 2013. Thesis, Hong Kong University of Science and Technology. Accessed March 09, 2021.
http://repository.ust.hk/ir/Record/1783.1-7976 ; https://doi.org/10.14711/thesis-b1240240 ; http://repository.ust.hk/ir/bitstream/1783.1-7976/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lu, Zhongqi. “Selective transfer learning for cross domain recommendation.” 2013. Web. 09 Mar 2021.
Vancouver:
Lu Z. Selective transfer learning for cross domain recommendation. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2013. [cited 2021 Mar 09].
Available from: http://repository.ust.hk/ir/Record/1783.1-7976 ; https://doi.org/10.14711/thesis-b1240240 ; http://repository.ust.hk/ir/bitstream/1783.1-7976/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lu Z. Selective transfer learning for cross domain recommendation. [Thesis]. Hong Kong University of Science and Technology; 2013. Available from: http://repository.ust.hk/ir/Record/1783.1-7976 ; https://doi.org/10.14711/thesis-b1240240 ; http://repository.ust.hk/ir/bitstream/1783.1-7976/1/th_redirect.html
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Hong Kong University of Science and Technology
29.
Lu, Zhongqi.
Temporal dynamics in recommender systems.
Degree: 2017, Hong Kong University of Science and Technology
URL: http://repository.ust.hk/ir/Record/1783.1-87955
;
https://doi.org/10.14711/thesis-b1778947
;
http://repository.ust.hk/ir/bitstream/1783.1-87955/1/th_redirect.html
► We investigate on the temporal dynamics phenomenon in recommender systems. By analyzing the public dataset from real world applications, we find the temporal dynamics phenomenon…
(more)
▼ We investigate on the temporal dynamics phenomenon in recommender systems. By analyzing the public dataset from real world applications, we find the temporal dynamics phenomenon is common in the online recommender systems, and the phenomenon would cause problems in making good recommendations. In this thesis, we propose four approaches to tackle the problems caused by the temporal dynamics phenomenon. The four approaches are the user’s autoregressive interests evolution, user’s markovian interests evolution, a POMDP recommendation framework, and the transfer learning approach. Both the user’s autoregressive interests evolution and the user’s markovian interests evolution are motivated by the sequential property in the changes of the user’s interests. The POMDP recommendation framework is inspired by the self-learning mechanism of reinforcement learning models. The transfer learning approach is driven by the rich source domain data. Overall, the four approaches focus on handling the problems raised by temporal dynamics phenomenon in recommender systems. We also discuss the metrics and the datasets to verify our proposed approaches.
Subjects/Keywords: Recommender systems (Information filtering)
; Mathematical models
; Analysis
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lu, Z. (2017). Temporal dynamics in recommender systems. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-87955 ; https://doi.org/10.14711/thesis-b1778947 ; http://repository.ust.hk/ir/bitstream/1783.1-87955/1/th_redirect.html
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):
Lu, Zhongqi. “Temporal dynamics in recommender systems.” 2017. Thesis, Hong Kong University of Science and Technology. Accessed March 09, 2021.
http://repository.ust.hk/ir/Record/1783.1-87955 ; https://doi.org/10.14711/thesis-b1778947 ; http://repository.ust.hk/ir/bitstream/1783.1-87955/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lu, Zhongqi. “Temporal dynamics in recommender systems.” 2017. Web. 09 Mar 2021.
Vancouver:
Lu Z. Temporal dynamics in recommender systems. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2017. [cited 2021 Mar 09].
Available from: http://repository.ust.hk/ir/Record/1783.1-87955 ; https://doi.org/10.14711/thesis-b1778947 ; http://repository.ust.hk/ir/bitstream/1783.1-87955/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lu Z. Temporal dynamics in recommender systems. [Thesis]. Hong Kong University of Science and Technology; 2017. Available from: http://repository.ust.hk/ir/Record/1783.1-87955 ; https://doi.org/10.14711/thesis-b1778947 ; http://repository.ust.hk/ir/bitstream/1783.1-87955/1/th_redirect.html
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Hong Kong University of Science and Technology
30.
Liu, Bo CSE.
Transferable bandit.
Degree: 2018, Hong Kong University of Science and Technology
URL: http://repository.ust.hk/ir/Record/1783.1-97741
;
https://doi.org/10.14711/thesis-991012675668103412
;
http://repository.ust.hk/ir/bitstream/1783.1-97741/1/th_redirect.html
► The booming development of Artificial Intelligence promotes a large number of online interactive services including recommender system (RecSys), online advertising, dialogue system, etc. These services…
(more)
▼ The booming development of Artificial Intelligence promotes a large number of online interactive services including recommender system (RecSys), online advertising, dialogue system, etc. These services require sophisticated algorithms to decide actions sequentially and to maximize the cumulative user feedback in the long run. To accomplish this goal, algorithms should simultaneously exploit and explore the user interests according to the partial and noisy user feedback. Bandit problem can successfully formulate the exploitation-exploration trade-off in these applications. When facing the insufficient observations in a target domain of interest, unfortunately, bandit policies may explore more than needed, which may lead to worse performance. In this thesis, we study a novel and challenging problem: Transferable Bandit. Via transfer learning, transferable bandit leverages prior knowledge from the existing source domains with sufficient user feedback to further optimize the cumulative rewards in the target domains of interest. Transferable bandit harness the collective and mutually reinforcing power of the bandit formulation and transfer learning. First, transfer learning improves the exploitation, accelerates its exploration, and balances the exploitation and exploration appropriately in the target domain. Second, the transferable bandit policy explores how to transfer and speeds up the knowledge transfer. This thesis addresses several critical challenges of transferable bandit problems. First, we propose Transferable Contextual Bandit (TCB) policy to bridge the action and context heterogeneity. Second, we present Lifelong Contextual Bandit (LCB) policy that sequentially transfers knowledge across homogeneous domains and maximizes overall cumulative rewards. Third, to facilitate the large-scale online deployment, we illustrate two speed up methods including stochastic approximation and feature selection. This thesis also presents a general framework based on the upper confidence bound principle to address the transferable bandit problem. Both empirical studies on real-world datasets and theoretical regret analysis validate this thesis.
Subjects/Keywords: Recommender systems (Information filtering)
; Data processing
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Liu, B. C. (2018). Transferable bandit. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-97741 ; https://doi.org/10.14711/thesis-991012675668103412 ; http://repository.ust.hk/ir/bitstream/1783.1-97741/1/th_redirect.html
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):
Liu, Bo CSE. “Transferable bandit.” 2018. Thesis, Hong Kong University of Science and Technology. Accessed March 09, 2021.
http://repository.ust.hk/ir/Record/1783.1-97741 ; https://doi.org/10.14711/thesis-991012675668103412 ; http://repository.ust.hk/ir/bitstream/1783.1-97741/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Liu, Bo CSE. “Transferable bandit.” 2018. Web. 09 Mar 2021.
Vancouver:
Liu BC. Transferable bandit. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2018. [cited 2021 Mar 09].
Available from: http://repository.ust.hk/ir/Record/1783.1-97741 ; https://doi.org/10.14711/thesis-991012675668103412 ; http://repository.ust.hk/ir/bitstream/1783.1-97741/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Liu BC. Transferable bandit. [Thesis]. Hong Kong University of Science and Technology; 2018. Available from: http://repository.ust.hk/ir/Record/1783.1-97741 ; https://doi.org/10.14711/thesis-991012675668103412 ; http://repository.ust.hk/ir/bitstream/1783.1-97741/1/th_redirect.html
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
◁ [1] [2] [3] [4] [5] … [16] ▶
. 
Open Access Theses and Dissertations