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Level: masters

You searched for subject:(Collaborative filtering). Showing records 1 – 30 of 37 total matches.

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Arizona State University

1. Magham, Venkatesh. Data Poisoning Attacks on Linked Data with Graph Regularization.

Degree: Computer Science, 2019, Arizona State University

 Social media has become the norm of everyone for communication. The usage of social media has increased exponentially in the last decade. The myriads of… (more)

Subjects/Keywords: Computer science; Information science; Collaborative filtering; Data poisoning attacks; Graph laplacian; Graph regularization; Linked data; Matrix factorization

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APA (6th Edition):

Magham, V. (2019). Data Poisoning Attacks on Linked Data with Graph Regularization. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/53572

Chicago Manual of Style (16th Edition):

Magham, Venkatesh. “Data Poisoning Attacks on Linked Data with Graph Regularization.” 2019. Masters Thesis, Arizona State University. Accessed July 23, 2019. http://repository.asu.edu/items/53572.

MLA Handbook (7th Edition):

Magham, Venkatesh. “Data Poisoning Attacks on Linked Data with Graph Regularization.” 2019. Web. 23 Jul 2019.

Vancouver:

Magham V. Data Poisoning Attacks on Linked Data with Graph Regularization. [Internet] [Masters thesis]. Arizona State University; 2019. [cited 2019 Jul 23]. Available from: http://repository.asu.edu/items/53572.

Council of Science Editors:

Magham V. Data Poisoning Attacks on Linked Data with Graph Regularization. [Masters Thesis]. Arizona State University; 2019. Available from: http://repository.asu.edu/items/53572


Delft University of Technology

2. Oldenzeel, P.R. Expertise Identification in Enterprise Social Media:.

Degree: 2012, Delft University of Technology

 The increasing adoption of Enterprise Social Media (ESM) systems within enterprises is driven by the need for the explicit facilitation of sharing expertise. Expertise Identification… (more)

Subjects/Keywords: expertise; expert; ESM; Enterprise Social Media; Collaborative Filtering; Expertise Identification; Enterprise 2.0; Expertise Identification; Expert Search; Social Tagging

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APA (6th Edition):

Oldenzeel, P. R. (2012). Expertise Identification in Enterprise Social Media:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5dfaca10-e09d-4d38-b52e-526b8307a17a

Chicago Manual of Style (16th Edition):

Oldenzeel, P R. “Expertise Identification in Enterprise Social Media:.” 2012. Masters Thesis, Delft University of Technology. Accessed July 23, 2019. http://resolver.tudelft.nl/uuid:5dfaca10-e09d-4d38-b52e-526b8307a17a.

MLA Handbook (7th Edition):

Oldenzeel, P R. “Expertise Identification in Enterprise Social Media:.” 2012. Web. 23 Jul 2019.

Vancouver:

Oldenzeel PR. Expertise Identification in Enterprise Social Media:. [Internet] [Masters thesis]. Delft University of Technology; 2012. [cited 2019 Jul 23]. Available from: http://resolver.tudelft.nl/uuid:5dfaca10-e09d-4d38-b52e-526b8307a17a.

Council of Science Editors:

Oldenzeel PR. Expertise Identification in Enterprise Social Media:. [Masters Thesis]. Delft University of Technology; 2012. Available from: http://resolver.tudelft.nl/uuid:5dfaca10-e09d-4d38-b52e-526b8307a17a

3. Rentmeester, M. Towards a Social Web based solution to bootstrap new domains in cross-domain recommendations:.

Degree: 2014, Delft University of Technology

 Most recommender systems recommend items from a single domain. However, usually users’ preferences span across multiple domains. Cross-domain recommender systems can successfully recommend items in… (more)

Subjects/Keywords: cross-domain recommendations; cold-start recommendations; recommender systems; Social Web; Open Images; YouTube; collaborative filtering; users' preferences

…domain recommendation using collaborative filtering in a situation of overlap… …computed in two ways, contentbased and using collaborative filtering. Current approaches use… …collaborative filtering (read: users’ preferences) to relate items from the new domain to… …standard collaborative filtering and we should come up with a new solution to solve this complex… …Collaborative filtering is the most used technique to solve that scenario. How this works is explained… 

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APA (6th Edition):

Rentmeester, M. (2014). Towards a Social Web based solution to bootstrap new domains in cross-domain recommendations:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0c5d66b1-1470-44e1-b1d6-e02de00c9b8b

Chicago Manual of Style (16th Edition):

Rentmeester, M. “Towards a Social Web based solution to bootstrap new domains in cross-domain recommendations:.” 2014. Masters Thesis, Delft University of Technology. Accessed July 23, 2019. http://resolver.tudelft.nl/uuid:0c5d66b1-1470-44e1-b1d6-e02de00c9b8b.

MLA Handbook (7th Edition):

Rentmeester, M. “Towards a Social Web based solution to bootstrap new domains in cross-domain recommendations:.” 2014. Web. 23 Jul 2019.

Vancouver:

Rentmeester M. Towards a Social Web based solution to bootstrap new domains in cross-domain recommendations:. [Internet] [Masters thesis]. Delft University of Technology; 2014. [cited 2019 Jul 23]. Available from: http://resolver.tudelft.nl/uuid:0c5d66b1-1470-44e1-b1d6-e02de00c9b8b.

Council of Science Editors:

Rentmeester M. Towards a Social Web based solution to bootstrap new domains in cross-domain recommendations:. [Masters Thesis]. Delft University of Technology; 2014. Available from: http://resolver.tudelft.nl/uuid:0c5d66b1-1470-44e1-b1d6-e02de00c9b8b


Kansas State University

4. Karanam, Manikanta Babu. Tackling the problems of diversity in recommender systems.

Degree: MS, Department of Computing and Information Sciences, 2010, Kansas State University

 A recommender system is a computational mechanism for information filtering, where users provide recommendations (in the form of ratings or selecting items) as inputs, which… (more)

Subjects/Keywords: Recommender Systems; Diversity; Collaborative Filtering; Content Based Filtering; Hybrid Systems; Computer Science (0984)

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APA (6th Edition):

Karanam, M. B. (2010). Tackling the problems of diversity in recommender systems. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/6981

Chicago Manual of Style (16th Edition):

Karanam, Manikanta Babu. “Tackling the problems of diversity in recommender systems.” 2010. Masters Thesis, Kansas State University. Accessed July 23, 2019. http://hdl.handle.net/2097/6981.

MLA Handbook (7th Edition):

Karanam, Manikanta Babu. “Tackling the problems of diversity in recommender systems.” 2010. Web. 23 Jul 2019.

Vancouver:

Karanam MB. Tackling the problems of diversity in recommender systems. [Internet] [Masters thesis]. Kansas State University; 2010. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/2097/6981.

Council of Science Editors:

Karanam MB. Tackling the problems of diversity in recommender systems. [Masters Thesis]. Kansas State University; 2010. Available from: http://hdl.handle.net/2097/6981


Missouri University of Science and Technology

5. Deokule, Shreyas S. A conceptual framework of using collaborative filtering algorithms to enhance keyword search.

Degree: M.S. in Information Science and Technology, Information Science and Technology, Missouri University of Science and Technology

"The main purpose of this research is to design a recommendation system, based on collaborative filtering, to aid users in searching research documents online. The recommendation system suggest appropriate keywords to the users which return better search results" – Abstract, leaf iii.

Subjects/Keywords: Collaborative filtering algorithms; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Deokule, S. S. (n.d.). A conceptual framework of using collaborative filtering algorithms to enhance keyword search. (Masters Thesis). Missouri University of Science and Technology. Retrieved from http://scholarsmine.mst.edu/masters_theses/4437

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Chicago Manual of Style (16th Edition):

Deokule, Shreyas S. “A conceptual framework of using collaborative filtering algorithms to enhance keyword search.” Masters Thesis, Missouri University of Science and Technology. Accessed July 23, 2019. http://scholarsmine.mst.edu/masters_theses/4437.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

MLA Handbook (7th Edition):

Deokule, Shreyas S. “A conceptual framework of using collaborative filtering algorithms to enhance keyword search.” Web. 23 Jul 2019.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Deokule SS. A conceptual framework of using collaborative filtering algorithms to enhance keyword search. [Internet] [Masters thesis]. Missouri University of Science and Technology; [cited 2019 Jul 23]. Available from: http://scholarsmine.mst.edu/masters_theses/4437.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Council of Science Editors:

Deokule SS. A conceptual framework of using collaborative filtering algorithms to enhance keyword search. [Masters Thesis]. Missouri University of Science and Technology; Available from: http://scholarsmine.mst.edu/masters_theses/4437

Note: this citation may be lacking information needed for this citation format:
No year of publication.


Penn State University

6. Gupta, Gaurav. PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS.

Degree: MS, Computer Science and Engineering, 2011, Penn State University

 Location Based Social Networks (LBSNs) have become popular among people in recent times. LBSN allow people to tag their presence at the places they visit,… (more)

Subjects/Keywords: location based social networks; recommendation systems; social network analysis; collaborative filtering; spatial databases

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APA (6th Edition):

Gupta, G. (2011). PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/11575

Chicago Manual of Style (16th Edition):

Gupta, Gaurav. “PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS.” 2011. Masters Thesis, Penn State University. Accessed July 23, 2019. https://etda.libraries.psu.edu/catalog/11575.

MLA Handbook (7th Edition):

Gupta, Gaurav. “PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS.” 2011. Web. 23 Jul 2019.

Vancouver:

Gupta G. PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS. [Internet] [Masters thesis]. Penn State University; 2011. [cited 2019 Jul 23]. Available from: https://etda.libraries.psu.edu/catalog/11575.

Council of Science Editors:

Gupta G. PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS. [Masters Thesis]. Penn State University; 2011. Available from: https://etda.libraries.psu.edu/catalog/11575


Penn State University

7. Ference, Gregory David. Location Recommendation for Mobile Users in Location-Based Social Networks.

Degree: MS, Computer Science and Engineering, 2013, Penn State University

 Location-based services have become popular in the twenty-first century due to technological advances, such as mobile and online social networking. One of its key features… (more)

Subjects/Keywords: Location Recommendation; Location-Based Social Networks; Collaborative Filtering; K-Nearest Diverse Neighbor; Spatial Diversity

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APA (6th Edition):

Ference, G. D. (2013). Location Recommendation for Mobile Users in Location-Based Social Networks. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/17382

Chicago Manual of Style (16th Edition):

Ference, Gregory David. “Location Recommendation for Mobile Users in Location-Based Social Networks.” 2013. Masters Thesis, Penn State University. Accessed July 23, 2019. https://etda.libraries.psu.edu/catalog/17382.

MLA Handbook (7th Edition):

Ference, Gregory David. “Location Recommendation for Mobile Users in Location-Based Social Networks.” 2013. Web. 23 Jul 2019.

Vancouver:

Ference GD. Location Recommendation for Mobile Users in Location-Based Social Networks. [Internet] [Masters thesis]. Penn State University; 2013. [cited 2019 Jul 23]. Available from: https://etda.libraries.psu.edu/catalog/17382.

Council of Science Editors:

Ference GD. Location Recommendation for Mobile Users in Location-Based Social Networks. [Masters Thesis]. Penn State University; 2013. Available from: https://etda.libraries.psu.edu/catalog/17382


Penn State University

8. Yao, Luqi. Study On Bipartite Network In Collaborative Filtering Recommender System.

Degree: MS, Industrial Engineering, 2015, Penn State University

 Recommender system is increasingly popular in recent years, scientists came up with plenty of recommendation algorithms and never stop trying to make the recommendation more… (more)

Subjects/Keywords: recommender system; bipartite network; collaborative filtering

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APA (6th Edition):

Yao, L. (2015). Study On Bipartite Network In Collaborative Filtering Recommender System. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/26335

Chicago Manual of Style (16th Edition):

Yao, Luqi. “Study On Bipartite Network In Collaborative Filtering Recommender System.” 2015. Masters Thesis, Penn State University. Accessed July 23, 2019. https://etda.libraries.psu.edu/catalog/26335.

MLA Handbook (7th Edition):

Yao, Luqi. “Study On Bipartite Network In Collaborative Filtering Recommender System.” 2015. Web. 23 Jul 2019.

Vancouver:

Yao L. Study On Bipartite Network In Collaborative Filtering Recommender System. [Internet] [Masters thesis]. Penn State University; 2015. [cited 2019 Jul 23]. Available from: https://etda.libraries.psu.edu/catalog/26335.

Council of Science Editors:

Yao L. Study On Bipartite Network In Collaborative Filtering Recommender System. [Masters Thesis]. Penn State University; 2015. Available from: https://etda.libraries.psu.edu/catalog/26335


Rochester Institute of Technology

9. Matus Nicodemos, Marcelo. Information-Based Neighborhood Modeling.

Degree: MS, Information Sciences and Technologies (GCCIS), 2017, Rochester Institute of Technology

  Since the inception of the World Wide Web, the amount of data present on websites and internet infrastructure has grown exponentially that researchers continuously… (more)

Subjects/Keywords: Collaborative filtering; Data; DIKW hierarchy; Information; k-Nearest neighbor; Recommender systems

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APA (6th Edition):

Matus Nicodemos, M. (2017). Information-Based Neighborhood Modeling. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9461

Chicago Manual of Style (16th Edition):

Matus Nicodemos, Marcelo. “Information-Based Neighborhood Modeling.” 2017. Masters Thesis, Rochester Institute of Technology. Accessed July 23, 2019. https://scholarworks.rit.edu/theses/9461.

MLA Handbook (7th Edition):

Matus Nicodemos, Marcelo. “Information-Based Neighborhood Modeling.” 2017. Web. 23 Jul 2019.

Vancouver:

Matus Nicodemos M. Information-Based Neighborhood Modeling. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2019 Jul 23]. Available from: https://scholarworks.rit.edu/theses/9461.

Council of Science Editors:

Matus Nicodemos M. Information-Based Neighborhood Modeling. [Masters Thesis]. Rochester Institute of Technology; 2017. Available from: https://scholarworks.rit.edu/theses/9461


San Jose State University

10. Shahab, Shehba. NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS.

Degree: MS, Computer Science, 2019, San Jose State University

  Skills-based hiring is a talent management approach that empowers employers to align recruitment around business results, rather than around credentials and title. It starts… (more)

Subjects/Keywords: Recommender Systems; Collaborative Filtering; Hybrid Approach; Job search; Artificial Intelligence and Robotics; Other Computer Sciences

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APA (6th Edition):

Shahab, S. (2019). NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS. (Masters Thesis). San Jose State University. Retrieved from https://scholarworks.sjsu.edu/etd_projects/684

Chicago Manual of Style (16th Edition):

Shahab, Shehba. “NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS.” 2019. Masters Thesis, San Jose State University. Accessed July 23, 2019. https://scholarworks.sjsu.edu/etd_projects/684.

MLA Handbook (7th Edition):

Shahab, Shehba. “NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS.” 2019. Web. 23 Jul 2019.

Vancouver:

Shahab S. NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2019 Jul 23]. Available from: https://scholarworks.sjsu.edu/etd_projects/684.

Council of Science Editors:

Shahab S. NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS. [Masters Thesis]. San Jose State University; 2019. Available from: https://scholarworks.sjsu.edu/etd_projects/684

11. Padmashali, Sarika. An Open Source Discussion Group Recommendation System.

Degree: MS, Computer Science, 2017, San Jose State University

  A recommendation system analyzes user behavior on a website to make suggestions about what a user should do in the future on the website.… (more)

Subjects/Keywords: collaborative filtering; recommendation systems; Artificial Intelligence and Robotics; Databases and Information Systems; Software Engineering

…40 VII LIST OF FIGURES Figure 1 Collaborative Filtering Technique… …developers of some rule-based recommendation systems expressed the phrase “collaborative filtering… …Based Filtering, Collaborative Filtering. During the initial stage of the project we tried a… …lot of collaborative filtering techniques such as baseline predictors, latent matrix… …Chapter 2 gives a background of collaborative filtering techniques. Chapter 3 discusses… 

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APA (6th Edition):

Padmashali, S. (2017). An Open Source Discussion Group Recommendation System. (Masters Thesis). San Jose State University. Retrieved from https://scholarworks.sjsu.edu/etd_projects/537

Chicago Manual of Style (16th Edition):

Padmashali, Sarika. “An Open Source Discussion Group Recommendation System.” 2017. Masters Thesis, San Jose State University. Accessed July 23, 2019. https://scholarworks.sjsu.edu/etd_projects/537.

MLA Handbook (7th Edition):

Padmashali, Sarika. “An Open Source Discussion Group Recommendation System.” 2017. Web. 23 Jul 2019.

Vancouver:

Padmashali S. An Open Source Discussion Group Recommendation System. [Internet] [Masters thesis]. San Jose State University; 2017. [cited 2019 Jul 23]. Available from: https://scholarworks.sjsu.edu/etd_projects/537.

Council of Science Editors:

Padmashali S. An Open Source Discussion Group Recommendation System. [Masters Thesis]. San Jose State University; 2017. Available from: https://scholarworks.sjsu.edu/etd_projects/537

12. Zeng, Jingying. Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering.

Degree: MS, Statistics, 2017, The Ohio State University

 Consider the problem of recommending products to a set of online users, where avery large selection of potential products are available. Recommender systems wereintroduced to… (more)

Subjects/Keywords: Statistics; Computer Science; Recommender Systems, Matrix Factorization models, Collaborative Filtering, Stochastic Gradient Descent

…and Sensitivity Analysis of the SGD Algorithm in Collaborative Filtering… …collaborative filtering, content-based filtering and hybrid recommender systems. The latter combines… …both collaborative filtering and content-based filtering together. Figure 1.1: An… …Meteren & Van Someren, 2000). The concept of collaborative filtering was introduced by… …order to recommend products. The collaborative filtering approach can be further divided into… 

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APA (6th Edition):

Zeng, J. (2017). Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1491255524283942

Chicago Manual of Style (16th Edition):

Zeng, Jingying. “Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering.” 2017. Masters Thesis, The Ohio State University. Accessed July 23, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1491255524283942.

MLA Handbook (7th Edition):

Zeng, Jingying. “Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering.” 2017. Web. 23 Jul 2019.

Vancouver:

Zeng J. Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering. [Internet] [Masters thesis]. The Ohio State University; 2017. [cited 2019 Jul 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1491255524283942.

Council of Science Editors:

Zeng J. Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering. [Masters Thesis]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1491255524283942

13. Neto, Joaquim. Abordagem multiagente em sistemas de recomendação Web .

Degree: 2015, Universidade Aberta

 O crescimento exponencial da informação disponível na Web torna difícil para os utilizadores a tarefa de obter a informação que pretendem e quando dela necessitam.… (more)

Subjects/Keywords: Informática; Páginas Web; Sistemas de recomendação; Internet; Web recommender systems; Multi-agent systems; Association rules; Collaborative filtering; JADE

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APA (6th Edition):

Neto, J. (2015). Abordagem multiagente em sistemas de recomendação Web . (Masters Thesis). Universidade Aberta. Retrieved from http://hdl.handle.net/10400.2/3896

Chicago Manual of Style (16th Edition):

Neto, Joaquim. “Abordagem multiagente em sistemas de recomendação Web .” 2015. Masters Thesis, Universidade Aberta. Accessed July 23, 2019. http://hdl.handle.net/10400.2/3896.

MLA Handbook (7th Edition):

Neto, Joaquim. “Abordagem multiagente em sistemas de recomendação Web .” 2015. Web. 23 Jul 2019.

Vancouver:

Neto J. Abordagem multiagente em sistemas de recomendação Web . [Internet] [Masters thesis]. Universidade Aberta; 2015. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/10400.2/3896.

Council of Science Editors:

Neto J. Abordagem multiagente em sistemas de recomendação Web . [Masters Thesis]. Universidade Aberta; 2015. Available from: http://hdl.handle.net/10400.2/3896


Universidade do Minho

14. Telha, Luana Geórgia Lopes. Smart Targeting de conteúdos para fidelizações e ofertas .

Degree: 2014, Universidade do Minho

 Os utilizadores de serviços informáticos recebem diariamente uma quantidade significativa e crescente de conteúdos não solicitados e muitas vezes não desejados. Este SPAM acontece através… (more)

Subjects/Keywords: Sistemas de recomendação; Filtragem colaborativa; Filtragem baseada em conteúdos; Filtragem híbrida; Cupões de oferta; Fidelização; Recommender systems; Collaborative filtering; Content-based filtering; hybrid filtering; Cuppons; Loyalty

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APA (6th Edition):

Telha, L. G. L. (2014). Smart Targeting de conteúdos para fidelizações e ofertas . (Masters Thesis). Universidade do Minho. Retrieved from http://hdl.handle.net/1822/33925

Chicago Manual of Style (16th Edition):

Telha, Luana Geórgia Lopes. “Smart Targeting de conteúdos para fidelizações e ofertas .” 2014. Masters Thesis, Universidade do Minho. Accessed July 23, 2019. http://hdl.handle.net/1822/33925.

MLA Handbook (7th Edition):

Telha, Luana Geórgia Lopes. “Smart Targeting de conteúdos para fidelizações e ofertas .” 2014. Web. 23 Jul 2019.

Vancouver:

Telha LGL. Smart Targeting de conteúdos para fidelizações e ofertas . [Internet] [Masters thesis]. Universidade do Minho; 2014. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/1822/33925.

Council of Science Editors:

Telha LGL. Smart Targeting de conteúdos para fidelizações e ofertas . [Masters Thesis]. Universidade do Minho; 2014. Available from: http://hdl.handle.net/1822/33925

15. AZUIRSON, Gabriel de Albuquerque Veloso. Investigação da combinação de filtragem colaborativa e recomendação baseada em confiança através de medidas de esparsidade .

Degree: 2015, Universidade Federal de Pernambuco

 Sistemas de recomendação têm desempenhado um papel importante em diferentes contextos de aplicação (e.g recomendação de produtos, filmes, músicas, livros, dentre outros). Eles automaticamente sugerem… (more)

Subjects/Keywords: Sistemas de Recomendação; Fatoração de Matriz; Filtragem Colaborativa; Sistemas de Recomendação Baseados em Confiança; Medidas de Esparsidade; Recommender Systems; Collaborative Filtering; Matrix Factorization; Trust-Based Recommendation Systems; Sparsity Measures

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APA (6th Edition):

AZUIRSON, G. d. A. V. (2015). Investigação da combinação de filtragem colaborativa e recomendação baseada em confiança através de medidas de esparsidade . (Masters Thesis). Universidade Federal de Pernambuco. Retrieved from https://repositorio.ufpe.br/handle/123456789/15900

Chicago Manual of Style (16th Edition):

AZUIRSON, Gabriel de Albuquerque Veloso. “Investigação da combinação de filtragem colaborativa e recomendação baseada em confiança através de medidas de esparsidade .” 2015. Masters Thesis, Universidade Federal de Pernambuco. Accessed July 23, 2019. https://repositorio.ufpe.br/handle/123456789/15900.

MLA Handbook (7th Edition):

AZUIRSON, Gabriel de Albuquerque Veloso. “Investigação da combinação de filtragem colaborativa e recomendação baseada em confiança através de medidas de esparsidade .” 2015. Web. 23 Jul 2019.

Vancouver:

AZUIRSON GdAV. Investigação da combinação de filtragem colaborativa e recomendação baseada em confiança através de medidas de esparsidade . [Internet] [Masters thesis]. Universidade Federal de Pernambuco; 2015. [cited 2019 Jul 23]. Available from: https://repositorio.ufpe.br/handle/123456789/15900.

Council of Science Editors:

AZUIRSON GdAV. Investigação da combinação de filtragem colaborativa e recomendação baseada em confiança através de medidas de esparsidade . [Masters Thesis]. Universidade Federal de Pernambuco; 2015. Available from: https://repositorio.ufpe.br/handle/123456789/15900


Universitetet i Tromsø

16. Mortensen, Magnus. Design and evaluation of a recommender system .

Degree: 2007, Universitetet i Tromsø

 In the recent years, the Web has undergone a tremendous growth regarding both content and users. This has lead to an information overload problem in… (more)

Subjects/Keywords: VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551; music; system; distribuert; internett; presisjon; anbefaling; filtrering; tilbakemelding; e-handel; tilbakemelding; brukere; skalerbarhet; recommender; filtering; collaborative; content-based; context; mood; feedback; precision; information overload; mean squared difference; web; e-commerce; retrieval; sparsity; intrusiveness; distributed; empirical; scalability; model; memory; mp3

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APA (6th Edition):

Mortensen, M. (2007). Design and evaluation of a recommender system . (Masters Thesis). Universitetet i Tromsø. Retrieved from http://hdl.handle.net/10037/762

Chicago Manual of Style (16th Edition):

Mortensen, Magnus. “Design and evaluation of a recommender system .” 2007. Masters Thesis, Universitetet i Tromsø. Accessed July 23, 2019. http://hdl.handle.net/10037/762.

MLA Handbook (7th Edition):

Mortensen, Magnus. “Design and evaluation of a recommender system .” 2007. Web. 23 Jul 2019.

Vancouver:

Mortensen M. Design and evaluation of a recommender system . [Internet] [Masters thesis]. Universitetet i Tromsø 2007. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/10037/762.

Council of Science Editors:

Mortensen M. Design and evaluation of a recommender system . [Masters Thesis]. Universitetet i Tromsø 2007. Available from: http://hdl.handle.net/10037/762


University of Alberta

17. Stepan, Torin KS. Incorporating Content and Context in Recommender Systems.

Degree: MS, Department of Electrical and Computer Engineering, 2015, University of Alberta

 Recommender systems are a growing area of research that find practical applications in a variety of domains. Integrated library systems and location-based social networks can… (more)

Subjects/Keywords: cold-start; hybrid; k-nearest neighbors; spatial; location based social networks; books; collaborative filtering; temporal; fuzzy; context; fuzzy taste vector; similarity; content; social; rural libraries; movies; recommendation; recommender; university digital libraries; classifier; locations

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APA (6th Edition):

Stepan, T. K. (2015). Incorporating Content and Context in Recommender Systems. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/qr46r3589

Chicago Manual of Style (16th Edition):

Stepan, Torin KS. “Incorporating Content and Context in Recommender Systems.” 2015. Masters Thesis, University of Alberta. Accessed July 23, 2019. https://era.library.ualberta.ca/files/qr46r3589.

MLA Handbook (7th Edition):

Stepan, Torin KS. “Incorporating Content and Context in Recommender Systems.” 2015. Web. 23 Jul 2019.

Vancouver:

Stepan TK. Incorporating Content and Context in Recommender Systems. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2019 Jul 23]. Available from: https://era.library.ualberta.ca/files/qr46r3589.

Council of Science Editors:

Stepan TK. Incorporating Content and Context in Recommender Systems. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/qr46r3589


University of Cincinnati

18. NARAYANASWAMY, SHRIRAM. A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS.

Degree: MS, Engineering : Computer Science, 2007, University of Cincinnati

 In today’s consumer driven world, people are faced with the problem of plenty. Choices abound everywhere, be it in movies, books or music. Recommender systems… (more)

Subjects/Keywords: Computer Science; collaborative filtering, recommender systems; lattice, concept, algorithm, Jester, Movielens

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APA (6th Edition):

NARAYANASWAMY, S. (2007). A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016

Chicago Manual of Style (16th Edition):

NARAYANASWAMY, SHRIRAM. “A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS.” 2007. Masters Thesis, University of Cincinnati. Accessed July 23, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016.

MLA Handbook (7th Edition):

NARAYANASWAMY, SHRIRAM. “A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS.” 2007. Web. 23 Jul 2019.

Vancouver:

NARAYANASWAMY S. A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS. [Internet] [Masters thesis]. University of Cincinnati; 2007. [cited 2019 Jul 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016.

Council of Science Editors:

NARAYANASWAMY S. A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS. [Masters Thesis]. University of Cincinnati; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016

19. Goyal, Vivek. A Recommendation System Based on Multiple Databases.

Degree: MS, Engineering and Applied Science: Computer Science, 2013, University of Cincinnati

 Recommendation Systems have long been serving the industry of e-commerce with recommendations pertaining to movies, books, travel packages et cetera. A user's activity or past… (more)

Subjects/Keywords: Computer Science; Collaborative Filtering; Similarity measures; Recommendation System; Neighborhood Model; Fuzzy Clustering; Data Mining

…14 1.2.1 1.2.2 Collaborative Filtering Recommendation Systems… …17 Chapter 2: Related Work 2.1 Neighborhood Models Based on Collaborative Filtering… …21 2.1.1 2.1.2 Item-based Collaborative Filtering… …23 2.1.3 2.2 User-based Collaborative Filtering… …24 Similarity Measures Used in Collaborative Filtering Technique… 

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APA (6th Edition):

Goyal, V. (2013). A Recommendation System Based on Multiple Databases. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581

Chicago Manual of Style (16th Edition):

Goyal, Vivek. “A Recommendation System Based on Multiple Databases.” 2013. Masters Thesis, University of Cincinnati. Accessed July 23, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581.

MLA Handbook (7th Edition):

Goyal, Vivek. “A Recommendation System Based on Multiple Databases.” 2013. Web. 23 Jul 2019.

Vancouver:

Goyal V. A Recommendation System Based on Multiple Databases. [Internet] [Masters thesis]. University of Cincinnati; 2013. [cited 2019 Jul 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581.

Council of Science Editors:

Goyal V. A Recommendation System Based on Multiple Databases. [Masters Thesis]. University of Cincinnati; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581


University of Florida

20. Alex, Daley. Enhanced Glade and Its Impact on Computational Data Analytics.

Degree: MS, Computer Engineering - Computer and Information Science and Engineering, 2012, University of Florida

 The management and analysis of large amounts of constantly increasing data is required to facilitate better knowledge and understanding. Such analysis extracts less apparent information… (more)

Subjects/Keywords: Aggregation; Analytics; Collaborative filtering; Databases; Datasets; Decision trees; Glades; Mining; Statistical mechanics; Statistics; datapath  – glade  – mahout  – mining

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APA (6th Edition):

Alex, D. (2012). Enhanced Glade and Its Impact on Computational Data Analytics. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044817

Chicago Manual of Style (16th Edition):

Alex, Daley. “Enhanced Glade and Its Impact on Computational Data Analytics.” 2012. Masters Thesis, University of Florida. Accessed July 23, 2019. http://ufdc.ufl.edu/UFE0044817.

MLA Handbook (7th Edition):

Alex, Daley. “Enhanced Glade and Its Impact on Computational Data Analytics.” 2012. Web. 23 Jul 2019.

Vancouver:

Alex D. Enhanced Glade and Its Impact on Computational Data Analytics. [Internet] [Masters thesis]. University of Florida; 2012. [cited 2019 Jul 23]. Available from: http://ufdc.ufl.edu/UFE0044817.

Council of Science Editors:

Alex D. Enhanced Glade and Its Impact on Computational Data Analytics. [Masters Thesis]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044817


University of Georgia

21. Fan, Xiaohu. A study of attacks on collaborative filter.

Degree: MS, Computer Science, 2011, University of Georgia

Collaborative filtering is a widely used technique to make classifications by using distributed feedback from all users. Recently, collaborative filtering has been proposed and used… (more)

Subjects/Keywords: Collaborative spam filtering

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APA (6th Edition):

Fan, X. (2011). A study of attacks on collaborative filter. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/fan_xiaohu_201105_ms

Chicago Manual of Style (16th Edition):

Fan, Xiaohu. “A study of attacks on collaborative filter.” 2011. Masters Thesis, University of Georgia. Accessed July 23, 2019. http://purl.galileo.usg.edu/uga_etd/fan_xiaohu_201105_ms.

MLA Handbook (7th Edition):

Fan, Xiaohu. “A study of attacks on collaborative filter.” 2011. Web. 23 Jul 2019.

Vancouver:

Fan X. A study of attacks on collaborative filter. [Internet] [Masters thesis]. University of Georgia; 2011. [cited 2019 Jul 23]. Available from: http://purl.galileo.usg.edu/uga_etd/fan_xiaohu_201105_ms.

Council of Science Editors:

Fan X. A study of attacks on collaborative filter. [Masters Thesis]. University of Georgia; 2011. Available from: http://purl.galileo.usg.edu/uga_etd/fan_xiaohu_201105_ms


University of Georgia

22. Jahangeer, Khalid. An open science approach to exploring time-accuracy trade-offs in recommender systems.

Degree: MS, Computer Science, 2017, University of Georgia

 Recommender Systems have become an integral part of our consumer dominated world. With the evolution of Big Data and the exponential expansion of consumerism it… (more)

Subjects/Keywords: Recommender Systems; Collaborative Filtering; Matrix Factorization; Singular Value Decomposition

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APA (6th Edition):

Jahangeer, K. (2017). An open science approach to exploring time-accuracy trade-offs in recommender systems. (Masters Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37801

Chicago Manual of Style (16th Edition):

Jahangeer, Khalid. “An open science approach to exploring time-accuracy trade-offs in recommender systems.” 2017. Masters Thesis, University of Georgia. Accessed July 23, 2019. http://hdl.handle.net/10724/37801.

MLA Handbook (7th Edition):

Jahangeer, Khalid. “An open science approach to exploring time-accuracy trade-offs in recommender systems.” 2017. Web. 23 Jul 2019.

Vancouver:

Jahangeer K. An open science approach to exploring time-accuracy trade-offs in recommender systems. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/10724/37801.

Council of Science Editors:

Jahangeer K. An open science approach to exploring time-accuracy trade-offs in recommender systems. [Masters Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/37801


University of Manitoba

23. Hashish, Yasmeen. "Kid-in-the-loop" content control: A collaborative and education-oriented content filtering approach.

Degree: Computer Science, 2014, University of Manitoba

 Given the proliferation of new-generation internet capable devices in our society, they are now commonly used for a variety of purposes and by a variety… (more)

Subjects/Keywords: Human-computer interaction; content filtering; parent-child collaboration; collaborative filtering

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APA (6th Edition):

Hashish, Y. (2014). "Kid-in-the-loop" content control: A collaborative and education-oriented content filtering approach. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/23556

Chicago Manual of Style (16th Edition):

Hashish, Yasmeen. “"Kid-in-the-loop" content control: A collaborative and education-oriented content filtering approach.” 2014. Masters Thesis, University of Manitoba. Accessed July 23, 2019. http://hdl.handle.net/1993/23556.

MLA Handbook (7th Edition):

Hashish, Yasmeen. “"Kid-in-the-loop" content control: A collaborative and education-oriented content filtering approach.” 2014. Web. 23 Jul 2019.

Vancouver:

Hashish Y. "Kid-in-the-loop" content control: A collaborative and education-oriented content filtering approach. [Internet] [Masters thesis]. University of Manitoba; 2014. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/1993/23556.

Council of Science Editors:

Hashish Y. "Kid-in-the-loop" content control: A collaborative and education-oriented content filtering approach. [Masters Thesis]. University of Manitoba; 2014. Available from: http://hdl.handle.net/1993/23556


University of New South Wales

24. Zhou, Bowen. Advanced Collaborative Filtering and Image-based Recommender Systems.

Degree: Computer Science & Engineering, 2017, University of New South Wales

 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)

Subjects/Keywords: Collaborative Filtering; Recommender Systems

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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 July 23, 2019. 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. 23 Jul 2019.

Vancouver:

Zhou B. Advanced Collaborative Filtering and Image-based Recommender Systems. [Internet] [Masters thesis]. University of New South Wales; 2017. [cited 2019 Jul 23]. 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 Notre Dame

25. Darcy A Davis. Predicting Individual Disease Risk Based on Medical History</h1>.

Degree: MSin Computer Science and Engineering, Computer Science and Engineering, 2008, University of Notre Dame

  The monumental cost of health care, especially for chronic disease treatment, is quickly becoming unmanageable. This crisis has motivated the drive towards preventative medicine,… (more)

Subjects/Keywords: collaborative filtering; disease prediction

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APA (6th Edition):

Davis, D. A. (2008). Predicting Individual Disease Risk Based on Medical History</h1>. (Masters Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/pg15bc40k5d

Chicago Manual of Style (16th Edition):

Davis, Darcy A. “Predicting Individual Disease Risk Based on Medical History</h1>.” 2008. Masters Thesis, University of Notre Dame. Accessed July 23, 2019. https://curate.nd.edu/show/pg15bc40k5d.

MLA Handbook (7th Edition):

Davis, Darcy A. “Predicting Individual Disease Risk Based on Medical History</h1>.” 2008. Web. 23 Jul 2019.

Vancouver:

Davis DA. Predicting Individual Disease Risk Based on Medical History</h1>. [Internet] [Masters thesis]. University of Notre Dame; 2008. [cited 2019 Jul 23]. Available from: https://curate.nd.edu/show/pg15bc40k5d.

Council of Science Editors:

Davis DA. Predicting Individual Disease Risk Based on Medical History</h1>. [Masters Thesis]. University of Notre Dame; 2008. Available from: https://curate.nd.edu/show/pg15bc40k5d

26. Κουτσόπουλος, Αθανάσιος. Ανάπτυξη εφαρμογής συνεργατικών συστάσεων βασισμένη σε οντολογίες για κινητές εμπορικές υπηρεσίες.

Degree: 2014, University of Patras

Στις μέρες μας η χρήση των κινητών συσκευών έχει σημειώσει αλματώδη ανάπτυξη και έχει γίνει αναπόσπαστο κομμάτι της καθημερινότητάς μας. Οι κινητές συσκευές με το… (more)

Subjects/Keywords: Συστημάτα συστάσεων; Οντολογίες; 006.332; Semantic Web; Collaborative filtering

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APA (6th Edition):

Κουτσόπουλος, . (2014). Ανάπτυξη εφαρμογής συνεργατικών συστάσεων βασισμένη σε οντολογίες για κινητές εμπορικές υπηρεσίες. (Masters Thesis). University of Patras. Retrieved from http://hdl.handle.net/10889/8334

Chicago Manual of Style (16th Edition):

Κουτσόπουλος, Αθανάσιος. “Ανάπτυξη εφαρμογής συνεργατικών συστάσεων βασισμένη σε οντολογίες για κινητές εμπορικές υπηρεσίες.” 2014. Masters Thesis, University of Patras. Accessed July 23, 2019. http://hdl.handle.net/10889/8334.

MLA Handbook (7th Edition):

Κουτσόπουλος, Αθανάσιος. “Ανάπτυξη εφαρμογής συνεργατικών συστάσεων βασισμένη σε οντολογίες για κινητές εμπορικές υπηρεσίες.” 2014. Web. 23 Jul 2019.

Vancouver:

Κουτσόπουλος . Ανάπτυξη εφαρμογής συνεργατικών συστάσεων βασισμένη σε οντολογίες για κινητές εμπορικές υπηρεσίες. [Internet] [Masters thesis]. University of Patras; 2014. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/10889/8334.

Council of Science Editors:

Κουτσόπουλος . Ανάπτυξη εφαρμογής συνεργατικών συστάσεων βασισμένη σε οντολογίες για κινητές εμπορικές υπηρεσίες. [Masters Thesis]. University of Patras; 2014. Available from: http://hdl.handle.net/10889/8334

27. Κουνέλη, Μαριάννα. Ανάπτυξη συστήματος συστάσεων συνεργατικής διήθησης με χρήση ιεραρχικών αλγορίθμων κατάταξης.

Degree: 2012, University of Patras

Σκοπός της παρούσας διπλωματικής διατριβής είναι η μελέτη και ανάπτυξη ενός νέου αλγοριθμικού πλαισίου Συνεργατικής Διήθησης(CF) για την παραγωγή συστάσεων. Η μέθοδος που προτείνουμε, βασίζεται… (more)

Subjects/Keywords: Συστήματα συστάσεων; Συνεργατική διήθηση; Αραιότητα; Σχεδόν πλήρης αναλυσιμότητα; Αλγόριθμοι κατάταξης; Πειράματα; 005.741; Recommender systems; Collaborative filtering; Sparsity; Near complete decomposability; Ranking algorithms; Experiments

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APA (6th Edition):

Κουνέλη, . (2012). Ανάπτυξη συστήματος συστάσεων συνεργατικής διήθησης με χρήση ιεραρχικών αλγορίθμων κατάταξης. (Masters Thesis). University of Patras. Retrieved from http://hdl.handle.net/10889/5826

Chicago Manual of Style (16th Edition):

Κουνέλη, Μαριάννα. “Ανάπτυξη συστήματος συστάσεων συνεργατικής διήθησης με χρήση ιεραρχικών αλγορίθμων κατάταξης.” 2012. Masters Thesis, University of Patras. Accessed July 23, 2019. http://hdl.handle.net/10889/5826.

MLA Handbook (7th Edition):

Κουνέλη, Μαριάννα. “Ανάπτυξη συστήματος συστάσεων συνεργατικής διήθησης με χρήση ιεραρχικών αλγορίθμων κατάταξης.” 2012. Web. 23 Jul 2019.

Vancouver:

Κουνέλη . Ανάπτυξη συστήματος συστάσεων συνεργατικής διήθησης με χρήση ιεραρχικών αλγορίθμων κατάταξης. [Internet] [Masters thesis]. University of Patras; 2012. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/10889/5826.

Council of Science Editors:

Κουνέλη . Ανάπτυξη συστήματος συστάσεων συνεργατικής διήθησης με χρήση ιεραρχικών αλγορίθμων κατάταξης. [Masters Thesis]. University of Patras; 2012. Available from: http://hdl.handle.net/10889/5826

28. Araujo, Tássia Camões. AppRecommender: um recomendador de aplicativos GNU/Linux.

Degree: Mestrado, Ciência da Computação, 2011, University of São Paulo

A crescente oferta de programas de código aberto na rede mundial de computadores expõe potenciais usuários a muitas possibilidades de escolha. Em face da pluralidade… (more)

Subjects/Keywords: aplicativos; applications; collaborative filtering; Debian GNU/Linux.; Debian GNU/Linux.; Debian packages; distribuições GNU/Linux; filtragem colaborativa; GNU/Linux distributions; pacotes Debian; Recommender systems; Sistemas de recomendação.

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APA (6th Edition):

Araujo, T. C. (2011). AppRecommender: um recomendador de aplicativos GNU/Linux. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/45/45134/tde-28092012-150655/ ;

Chicago Manual of Style (16th Edition):

Araujo, Tássia Camões. “AppRecommender: um recomendador de aplicativos GNU/Linux.” 2011. Masters Thesis, University of São Paulo. Accessed July 23, 2019. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-28092012-150655/ ;.

MLA Handbook (7th Edition):

Araujo, Tássia Camões. “AppRecommender: um recomendador de aplicativos GNU/Linux.” 2011. Web. 23 Jul 2019.

Vancouver:

Araujo TC. AppRecommender: um recomendador de aplicativos GNU/Linux. [Internet] [Masters thesis]. University of São Paulo; 2011. [cited 2019 Jul 23]. Available from: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-28092012-150655/ ;.

Council of Science Editors:

Araujo TC. AppRecommender: um recomendador de aplicativos GNU/Linux. [Masters Thesis]. University of São Paulo; 2011. Available from: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-28092012-150655/ ;

29. Heshmat Dehkordi, Yasamin. Incorporating User Reviews as Implicit Feedback for Improving Recommender Systems.

Degree: Department of Computer Science, 2014, University of Victoria

 Recommendation systems have become extremely common in recent years due to the ubiquity of information across various applications. Online entertainment (e.g., Netflix), E-commerce (e.g., Amazon,… (more)

Subjects/Keywords: recommender systems; collaborative filtering; performance metrics; Yelp data set

Collaborative Filtering (CF) [14] has become one of the most popular approaches… …as Amazon, iTunes and Netflix are 2 among the services that use collaborative filtering… …method for recommendation. Collaborative filtering depends on wisdom of the crowd. These… …x5B;2] is one of the approaches in collaborative filtering that finds other users (… …based on the user-based collaborative filtering techniques [2] and Koren Bell’s… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Heshmat Dehkordi, Y. (2014). Incorporating User Reviews as Implicit Feedback for Improving Recommender Systems. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/5605

Chicago Manual of Style (16th Edition):

Heshmat Dehkordi, Yasamin. “Incorporating User Reviews as Implicit Feedback for Improving Recommender Systems.” 2014. Masters Thesis, University of Victoria. Accessed July 23, 2019. http://hdl.handle.net/1828/5605.

MLA Handbook (7th Edition):

Heshmat Dehkordi, Yasamin. “Incorporating User Reviews as Implicit Feedback for Improving Recommender Systems.” 2014. Web. 23 Jul 2019.

Vancouver:

Heshmat Dehkordi Y. Incorporating User Reviews as Implicit Feedback for Improving Recommender Systems. [Internet] [Masters thesis]. University of Victoria; 2014. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/1828/5605.

Council of Science Editors:

Heshmat Dehkordi Y. Incorporating User Reviews as Implicit Feedback for Improving Recommender Systems. [Masters Thesis]. University of Victoria; 2014. Available from: http://hdl.handle.net/1828/5605

30. Khezrzadeh, Maryam. Harnessing the power of "favorites" lists for recommendation systems.

Degree: Dept. of Computer Science, 2010, University of Victoria

 This thesis proposes a novel recommendation approach to take advantage of the information available in user-created lists. Our approach assumes associations among any two items… (more)

Subjects/Keywords: Recommendation system; Association analysis; Amazon; Frequency; Bayesian rating; Collaborative filtering; CIRC; UVic Subject Index::Sciences and Engineering::Applied Sciences::Computer science

…effectively and that limited the practical usage of CB systems. The collaborative Filtering (CF… …Collaborative filtering recommendation algorithms find similar users based on their previous… …collaborative filtering recommendation algorithms are claimed to address these two challenges… …over item-based and user-based collaborative filtering approaches Chapter 6 contains a… …Collaborative Filtering and Hybrid Methods. Each of these methods will be addressed in one of the… 

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Khezrzadeh, M. (2010). Harnessing the power of "favorites" lists for recommendation systems. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/2047

Chicago Manual of Style (16th Edition):

Khezrzadeh, Maryam. “Harnessing the power of "favorites" lists for recommendation systems.” 2010. Masters Thesis, University of Victoria. Accessed July 23, 2019. http://hdl.handle.net/1828/2047.

MLA Handbook (7th Edition):

Khezrzadeh, Maryam. “Harnessing the power of "favorites" lists for recommendation systems.” 2010. Web. 23 Jul 2019.

Vancouver:

Khezrzadeh M. Harnessing the power of "favorites" lists for recommendation systems. [Internet] [Masters thesis]. University of Victoria; 2010. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/1828/2047.

Council of Science Editors:

Khezrzadeh M. Harnessing the power of "favorites" lists for recommendation systems. [Masters Thesis]. University of Victoria; 2010. Available from: http://hdl.handle.net/1828/2047

[1] [2]

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