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Level: doctoral

You searched for subject:(Collaborative filtering). Showing records 1 – 30 of 40 total matches.

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1. Todeschini, Adrien. Probabilistic and Bayesian nonparametric approaches for recommender systems and networks : Approches probabilistes et bayésiennes non paramétriques pour les systemes de recommandation et les réseaux.

Degree: Docteur es, Mathématiques appliquées et calcul scientifique, 2016, Bordeaux

Nous proposons deux nouvelles approches pour les systèmes de recommandation et les réseaux. Dans la première partie, nous donnons d’abord un aperçu sur les systèmes… (more)

Subjects/Keywords: Systèmes de recommandation; Filtrage collaboratif; Complétion de matrice de rang faible; Modèles probabilistes; Espérance-maximisation; Réseaux; Parcimonie; Comportement en loi de puissance; Structure en communautés; Méthodes bayésiennes non paramétriques; Mesures complètement aléatoires; Monte Carlo par chaîne de Markov; Graphes; Recommender systems; Collaborative filtering; Low-rank matrix completion; Probabilistic models; Expectation maximization; Networks; Graphs; Sparsity; Power-law behavior; Community structure; Bayesian nonparametrics; Completely random measures; Markov chain Monte Carlo

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

APA (6th Edition):

Todeschini, A. (2016). Probabilistic and Bayesian nonparametric approaches for recommender systems and networks : Approches probabilistes et bayésiennes non paramétriques pour les systemes de recommandation et les réseaux. (Doctoral Dissertation). Bordeaux. Retrieved from http://www.theses.fr/2016BORD0237

Chicago Manual of Style (16th Edition):

Todeschini, Adrien. “Probabilistic and Bayesian nonparametric approaches for recommender systems and networks : Approches probabilistes et bayésiennes non paramétriques pour les systemes de recommandation et les réseaux.” 2016. Doctoral Dissertation, Bordeaux. Accessed July 15, 2019. http://www.theses.fr/2016BORD0237.

MLA Handbook (7th Edition):

Todeschini, Adrien. “Probabilistic and Bayesian nonparametric approaches for recommender systems and networks : Approches probabilistes et bayésiennes non paramétriques pour les systemes de recommandation et les réseaux.” 2016. Web. 15 Jul 2019.

Vancouver:

Todeschini A. Probabilistic and Bayesian nonparametric approaches for recommender systems and networks : Approches probabilistes et bayésiennes non paramétriques pour les systemes de recommandation et les réseaux. [Internet] [Doctoral dissertation]. Bordeaux; 2016. [cited 2019 Jul 15]. Available from: http://www.theses.fr/2016BORD0237.

Council of Science Editors:

Todeschini A. Probabilistic and Bayesian nonparametric approaches for recommender systems and networks : Approches probabilistes et bayésiennes non paramétriques pour les systemes de recommandation et les réseaux. [Doctoral Dissertation]. Bordeaux; 2016. Available from: http://www.theses.fr/2016BORD0237


Brunel University

2. Ryding, Michael Philip. The collaborative index.

Degree: PhD, 2006, Brunel University

 Information-seekers use a variety of information stores including electronic systems and the physical world experience of their community. Within electronic systems, information-seekers often report feelings… (more)

Subjects/Keywords: 020; Information stores; Information overload; Collaborative filtering; Recommender systems; Social navigation

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APA (6th Edition):

Ryding, M. P. (2006). The collaborative index. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/5481 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487018

Chicago Manual of Style (16th Edition):

Ryding, Michael Philip. “The collaborative index.” 2006. Doctoral Dissertation, Brunel University. Accessed July 15, 2019. http://bura.brunel.ac.uk/handle/2438/5481 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487018.

MLA Handbook (7th Edition):

Ryding, Michael Philip. “The collaborative index.” 2006. Web. 15 Jul 2019.

Vancouver:

Ryding MP. The collaborative index. [Internet] [Doctoral dissertation]. Brunel University; 2006. [cited 2019 Jul 15]. Available from: http://bura.brunel.ac.uk/handle/2438/5481 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487018.

Council of Science Editors:

Ryding MP. The collaborative index. [Doctoral Dissertation]. Brunel University; 2006. Available from: http://bura.brunel.ac.uk/handle/2438/5481 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487018

3. Vu, Xuan Truong. User-centered and group-based approach for social data filtering and sharing : Approche centrée utilisateur et basée groupe d'intérêt pour filtrer et partager des données sociales.

Degree: Docteur es, Technologies de l'Information et des Systèmes, 2015, Compiègne

Les médias sociaux occupent un rôle grandissant dans de nombreux domaines de notre vie quotidienne. Parmi d'autres, les réseaux sociaux tels que Facebook, Twitter, LinkedIn… (more)

Subjects/Keywords: Agrégation de données sociales; Réseaux sociaux en ligne; Extraction d'information; Groupes d'intérêt; Système collaboratif; Communauté virtuelle; Social media; Social network sites; Social data aggregation; Information filtering; Groups of interest; Collaborative system; 620

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APA (6th Edition):

Vu, X. T. (2015). User-centered and group-based approach for social data filtering and sharing : Approche centrée utilisateur et basée groupe d'intérêt pour filtrer et partager des données sociales. (Doctoral Dissertation). Compiègne. Retrieved from http://www.theses.fr/2015COMP2179

Chicago Manual of Style (16th Edition):

Vu, Xuan Truong. “User-centered and group-based approach for social data filtering and sharing : Approche centrée utilisateur et basée groupe d'intérêt pour filtrer et partager des données sociales.” 2015. Doctoral Dissertation, Compiègne. Accessed July 15, 2019. http://www.theses.fr/2015COMP2179.

MLA Handbook (7th Edition):

Vu, Xuan Truong. “User-centered and group-based approach for social data filtering and sharing : Approche centrée utilisateur et basée groupe d'intérêt pour filtrer et partager des données sociales.” 2015. Web. 15 Jul 2019.

Vancouver:

Vu XT. User-centered and group-based approach for social data filtering and sharing : Approche centrée utilisateur et basée groupe d'intérêt pour filtrer et partager des données sociales. [Internet] [Doctoral dissertation]. Compiègne; 2015. [cited 2019 Jul 15]. Available from: http://www.theses.fr/2015COMP2179.

Council of Science Editors:

Vu XT. User-centered and group-based approach for social data filtering and sharing : Approche centrée utilisateur et basée groupe d'intérêt pour filtrer et partager des données sociales. [Doctoral Dissertation]. Compiègne; 2015. Available from: http://www.theses.fr/2015COMP2179


Delft University of Technology

4. Loni, B. Advanced Factorization Models for Recommender Systems.

Degree: 2018, Delft University of Technology

 Recommender Systems have become a crucial tool to serve personalized content and to promote online products and media, but also to recommend restaurants, events, news… (more)

Subjects/Keywords: Factorization Models; Collaborative Filtering; Recommender Systems

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APA (6th Edition):

Loni, B. (2018). Advanced Factorization Models for Recommender Systems. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:NBN:nl:ui:24-uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; 0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:isbn:978-94-6375-232-9 ; 10.4233/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:NBN:nl:ui:24-uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; http://resolver.tudelft.nl/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81

Chicago Manual of Style (16th Edition):

Loni, B. “Advanced Factorization Models for Recommender Systems.” 2018. Doctoral Dissertation, Delft University of Technology. Accessed July 15, 2019. http://resolver.tudelft.nl/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:NBN:nl:ui:24-uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; 0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:isbn:978-94-6375-232-9 ; 10.4233/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:NBN:nl:ui:24-uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; http://resolver.tudelft.nl/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81.

MLA Handbook (7th Edition):

Loni, B. “Advanced Factorization Models for Recommender Systems.” 2018. Web. 15 Jul 2019.

Vancouver:

Loni B. Advanced Factorization Models for Recommender Systems. [Internet] [Doctoral dissertation]. Delft University of Technology; 2018. [cited 2019 Jul 15]. Available from: http://resolver.tudelft.nl/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:NBN:nl:ui:24-uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; 0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:isbn:978-94-6375-232-9 ; 10.4233/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:NBN:nl:ui:24-uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; http://resolver.tudelft.nl/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81.

Council of Science Editors:

Loni B. Advanced Factorization Models for Recommender Systems. [Doctoral Dissertation]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:NBN:nl:ui:24-uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; 0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:isbn:978-94-6375-232-9 ; 10.4233/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; urn:NBN:nl:ui:24-uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81 ; http://resolver.tudelft.nl/uuid:0b91c68f-4da7-4745-8d08-c39c0bb00e81


Delft University of Technology

5. Shi, Y. Ranking and Context-awareness in Recommender Systems.

Degree: 2013, Delft University of Technology

 In this thesis we report the results of our research on recommender systems, which addresses some of the critical scientific challenges that still remain open… (more)

Subjects/Keywords: Collaborative filtering; Recommender systems; Context-awareness; Ranking

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APA (6th Edition):

Shi, Y. (2013). Ranking and Context-awareness in Recommender Systems. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; urn:NBN:nl:ui:24-uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; urn:NBN:nl:ui:24-uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; http://resolver.tudelft.nl/uuid:f7d3977e-f191-40d4-8f27-784a32902a55

Chicago Manual of Style (16th Edition):

Shi, Y. “Ranking and Context-awareness in Recommender Systems.” 2013. Doctoral Dissertation, Delft University of Technology. Accessed July 15, 2019. http://resolver.tudelft.nl/uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; urn:NBN:nl:ui:24-uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; urn:NBN:nl:ui:24-uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; http://resolver.tudelft.nl/uuid:f7d3977e-f191-40d4-8f27-784a32902a55.

MLA Handbook (7th Edition):

Shi, Y. “Ranking and Context-awareness in Recommender Systems.” 2013. Web. 15 Jul 2019.

Vancouver:

Shi Y. Ranking and Context-awareness in Recommender Systems. [Internet] [Doctoral dissertation]. Delft University of Technology; 2013. [cited 2019 Jul 15]. Available from: http://resolver.tudelft.nl/uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; urn:NBN:nl:ui:24-uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; urn:NBN:nl:ui:24-uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; http://resolver.tudelft.nl/uuid:f7d3977e-f191-40d4-8f27-784a32902a55.

Council of Science Editors:

Shi Y. Ranking and Context-awareness in Recommender Systems. [Doctoral Dissertation]. Delft University of Technology; 2013. Available from: http://resolver.tudelft.nl/uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; urn:NBN:nl:ui:24-uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; urn:NBN:nl:ui:24-uuid:f7d3977e-f191-40d4-8f27-784a32902a55 ; http://resolver.tudelft.nl/uuid:f7d3977e-f191-40d4-8f27-784a32902a55


Delft University of Technology

6. Clements, M. Personalised Access to Social Media.

Degree: 2010, Delft University of Technology

 On many websites users can personally contribute information, ranging from short text messages to photos and videos. Users can see the information contributed by others… (more)

Subjects/Keywords: social media; information retrieval; web2.0; machine learning; data analysis; collaborative filtering; geotag

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APA (6th Edition):

Clements, M. (2010). Personalised Access to Social Media. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; urn:NBN:nl:ui:24-uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; urn:NBN:nl:ui:24-uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; http://resolver.tudelft.nl/uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3

Chicago Manual of Style (16th Edition):

Clements, M. “Personalised Access to Social Media.” 2010. Doctoral Dissertation, Delft University of Technology. Accessed July 15, 2019. http://resolver.tudelft.nl/uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; urn:NBN:nl:ui:24-uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; urn:NBN:nl:ui:24-uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; http://resolver.tudelft.nl/uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3.

MLA Handbook (7th Edition):

Clements, M. “Personalised Access to Social Media.” 2010. Web. 15 Jul 2019.

Vancouver:

Clements M. Personalised Access to Social Media. [Internet] [Doctoral dissertation]. Delft University of Technology; 2010. [cited 2019 Jul 15]. Available from: http://resolver.tudelft.nl/uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; urn:NBN:nl:ui:24-uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; urn:NBN:nl:ui:24-uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; http://resolver.tudelft.nl/uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3.

Council of Science Editors:

Clements M. Personalised Access to Social Media. [Doctoral Dissertation]. Delft University of Technology; 2010. Available from: http://resolver.tudelft.nl/uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; urn:NBN:nl:ui:24-uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; urn:NBN:nl:ui:24-uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3 ; http://resolver.tudelft.nl/uuid:8ae9dab3-c7e1-4329-bb15-103a085e0de3


Euskal Herriko Unibertsitatea / Universidad del País Vasco

7. Perona Balda, Iñigo. Behaviour modelling with data obtained from the Internet and contributions to cluster validation .

Degree: 2016, Euskal Herriko Unibertsitatea / Universidad del País Vasco

 [EN]This PhD thesis makes contributions in modelling behaviours found in different types of data acquired from the Internet and in the field of clustering evaluation.… (more)

Subjects/Keywords: behavioural modelling; data mining; pattern discovery; machine learning; unsupervised anomaly detection; clustering; cluster validity indices; suffix trees; sequential pattern mining; web personalization; user profiling; collaborative filtering; web mining; web log analysis; link prediction; network Intrusion Detection Systems (nIDS); tourism website mining; profiling disabled people

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APA (6th Edition):

Perona Balda, I. (2016). Behaviour modelling with data obtained from the Internet and contributions to cluster validation . (Doctoral Dissertation). Euskal Herriko Unibertsitatea / Universidad del País Vasco. Retrieved from http://hdl.handle.net/10810/20620

Chicago Manual of Style (16th Edition):

Perona Balda, Iñigo. “Behaviour modelling with data obtained from the Internet and contributions to cluster validation .” 2016. Doctoral Dissertation, Euskal Herriko Unibertsitatea / Universidad del País Vasco. Accessed July 15, 2019. http://hdl.handle.net/10810/20620.

MLA Handbook (7th Edition):

Perona Balda, Iñigo. “Behaviour modelling with data obtained from the Internet and contributions to cluster validation .” 2016. Web. 15 Jul 2019.

Vancouver:

Perona Balda I. Behaviour modelling with data obtained from the Internet and contributions to cluster validation . [Internet] [Doctoral dissertation]. Euskal Herriko Unibertsitatea / Universidad del País Vasco; 2016. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/10810/20620.

Council of Science Editors:

Perona Balda I. Behaviour modelling with data obtained from the Internet and contributions to cluster validation . [Doctoral Dissertation]. Euskal Herriko Unibertsitatea / Universidad del País Vasco; 2016. Available from: http://hdl.handle.net/10810/20620


Florida International University

8. Zeng, Kaiman. Next Generation of Product Search and Discovery.

Degree: PhD, Electrical Engineering, 2015, Florida International University

  Online shopping has become an important part of people’s daily life with the rapid development of e-commerce. In some domains such as books, electronics,… (more)

Subjects/Keywords: visual search; content based image retrieval; ranking; hypergraph learning; recommendation; collaborative filtering; clustering; Other Electrical and Computer Engineering; Signal Processing

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APA (6th Edition):

Zeng, K. (2015). Next Generation of Product Search and Discovery. (Doctoral Dissertation). Florida International University. Retrieved from http://digitalcommons.fiu.edu/etd/2312 ; 10.25148/etd.FIDC000207 ; FIDC000207

Chicago Manual of Style (16th Edition):

Zeng, Kaiman. “Next Generation of Product Search and Discovery.” 2015. Doctoral Dissertation, Florida International University. Accessed July 15, 2019. http://digitalcommons.fiu.edu/etd/2312 ; 10.25148/etd.FIDC000207 ; FIDC000207.

MLA Handbook (7th Edition):

Zeng, Kaiman. “Next Generation of Product Search and Discovery.” 2015. Web. 15 Jul 2019.

Vancouver:

Zeng K. Next Generation of Product Search and Discovery. [Internet] [Doctoral dissertation]. Florida International University; 2015. [cited 2019 Jul 15]. Available from: http://digitalcommons.fiu.edu/etd/2312 ; 10.25148/etd.FIDC000207 ; FIDC000207.

Council of Science Editors:

Zeng K. Next Generation of Product Search and Discovery. [Doctoral Dissertation]. Florida International University; 2015. Available from: http://digitalcommons.fiu.edu/etd/2312 ; 10.25148/etd.FIDC000207 ; FIDC000207

9. Hou, Hailong. Computing with Granular Words.

Degree: PhD, Computer Science, 2011, Georgia State University

  Computational linguistics is a sub-field of artificial intelligence; it is an interdisciplinary field dealing with statistical and/or rule-based modeling of natural language from a… (more)

Subjects/Keywords: Computing with word; Granular word; Granular information hyper tree; Spam filtering; Recommendation system; Collaborative intelligence.

…computing with granular word and CWGW based collaborative filtering algorithm are proposed in this… …35 CHAPTER 5 GIHT BASED BAYESIAN ALGORITHM FOR SPAM FILTERING… …72 ix LIST OF FIGURES Figure 1.1 Compare spam filtering algorithms… …52 Figure 6.2 Collaborative Intelligence System… …recent years, it has been applied to multiple areas such as E-mail filtering, semantic web… 

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APA (6th Edition):

Hou, H. (2011). Computing with Granular Words. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/cs_theses/73

Chicago Manual of Style (16th Edition):

Hou, Hailong. “Computing with Granular Words.” 2011. Doctoral Dissertation, Georgia State University. Accessed July 15, 2019. https://scholarworks.gsu.edu/cs_theses/73.

MLA Handbook (7th Edition):

Hou, Hailong. “Computing with Granular Words.” 2011. Web. 15 Jul 2019.

Vancouver:

Hou H. Computing with Granular Words. [Internet] [Doctoral dissertation]. Georgia State University; 2011. [cited 2019 Jul 15]. Available from: https://scholarworks.gsu.edu/cs_theses/73.

Council of Science Editors:

Hou H. Computing with Granular Words. [Doctoral Dissertation]. Georgia State University; 2011. Available from: https://scholarworks.gsu.edu/cs_theses/73


Georgia Tech

10. Zhou, Ke. Extending low-rank matrix factorizations for emerging applications.

Degree: PhD, Computational Science and Engineering, 2013, Georgia Tech

 Low-rank matrix factorizations have become increasingly popular to project high dimensional data into latent spaces with small dimensions in order to obtain better understandings of… (more)

Subjects/Keywords: Matrix factorization; Collaborative filtering; Social network; Dimensional analysis Computer programs; Cluster analysis Data processing; Social networks

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APA (6th Edition):

Zhou, K. (2013). Extending low-rank matrix factorizations for emerging applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/50230

Chicago Manual of Style (16th Edition):

Zhou, Ke. “Extending low-rank matrix factorizations for emerging applications.” 2013. Doctoral Dissertation, Georgia Tech. Accessed July 15, 2019. http://hdl.handle.net/1853/50230.

MLA Handbook (7th Edition):

Zhou, Ke. “Extending low-rank matrix factorizations for emerging applications.” 2013. Web. 15 Jul 2019.

Vancouver:

Zhou K. Extending low-rank matrix factorizations for emerging applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/1853/50230.

Council of Science Editors:

Zhou K. Extending low-rank matrix factorizations for emerging applications. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/50230


Georgia Tech

11. Parameswaran, Rupa. A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering.

Degree: PhD, Electrical and Computer Engineering, 2006, Georgia Tech

 Privacy is defined as the freedom from unauthorized intrusion. The availability of personal information through online databases, such as government records, medical records, and voters… (more)

Subjects/Keywords: Data privacy; Data obfuscation; Data mining; Collaborative filtering; Data mining; Information storage and retrieval systems; Cluster analysis Computer programs; Database security

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APA (6th Edition):

Parameswaran, R. (2006). A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/11459

Chicago Manual of Style (16th Edition):

Parameswaran, Rupa. “A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering.” 2006. Doctoral Dissertation, Georgia Tech. Accessed July 15, 2019. http://hdl.handle.net/1853/11459.

MLA Handbook (7th Edition):

Parameswaran, Rupa. “A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering.” 2006. Web. 15 Jul 2019.

Vancouver:

Parameswaran R. A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering. [Internet] [Doctoral dissertation]. Georgia Tech; 2006. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/1853/11459.

Council of Science Editors:

Parameswaran R. A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering. [Doctoral Dissertation]. Georgia Tech; 2006. Available from: http://hdl.handle.net/1853/11459


Georgia Tech

12. Lee, Joonseok. Local approaches for collaborative filtering.

Degree: PhD, Computer Science, 2015, Georgia Tech

 Recommendation systems are emerging as an important business application as the demand for personalized services in E-commerce increases. Collaborative filtering techniques are widely used for… (more)

Subjects/Keywords: Recommendation systems; Collaborative filtering; Machine learning; Local low-rank assumption; Matrix factorization; Matrix approximation; Ensemble collaborative ranking

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APA (6th Edition):

Lee, J. (2015). Local approaches for collaborative filtering. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53846

Chicago Manual of Style (16th Edition):

Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Doctoral Dissertation, Georgia Tech. Accessed July 15, 2019. http://hdl.handle.net/1853/53846.

MLA Handbook (7th Edition):

Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Web. 15 Jul 2019.

Vancouver:

Lee J. Local approaches for collaborative filtering. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/1853/53846.

Council of Science Editors:

Lee J. Local approaches for collaborative filtering. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53846


Georgia Tech

13. Yu, Hong. A data-driven approach for personalized drama management.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 An interactive narrative is a form of digital entertainment in which players can create or influence a dramatic storyline through actions, typically by assuming the… (more)

Subjects/Keywords: Personalized drama manager; Interactive narrative; Player modeling; Prefix based collaborative filtering

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APA (6th Edition):

Yu, H. (2015). A data-driven approach for personalized drama management. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53851

Chicago Manual of Style (16th Edition):

Yu, Hong. “A data-driven approach for personalized drama management.” 2015. Doctoral Dissertation, Georgia Tech. Accessed July 15, 2019. http://hdl.handle.net/1853/53851.

MLA Handbook (7th Edition):

Yu, Hong. “A data-driven approach for personalized drama management.” 2015. Web. 15 Jul 2019.

Vancouver:

Yu H. A data-driven approach for personalized drama management. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/1853/53851.

Council of Science Editors:

Yu H. A data-driven approach for personalized drama management. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53851


Georgia Tech

14. Zou, Jun. Social computing for personalization and credible information mining using probabilistic graphical models.

Degree: PhD, Electrical and Computer Engineering, 2016, Georgia Tech

 In this dissertation, we address challenging social computing problems in personalized recommender systems and social media information mining. We tap into probabilistic graphical models, including… (more)

Subjects/Keywords: Social computing; Recommender systems; Collaborative filtering; Belief propagation; Probabilistic graphical models

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APA (6th Edition):

Zou, J. (2016). Social computing for personalization and credible information mining using probabilistic graphical models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55646

Chicago Manual of Style (16th Edition):

Zou, Jun. “Social computing for personalization and credible information mining using probabilistic graphical models.” 2016. Doctoral Dissertation, Georgia Tech. Accessed July 15, 2019. http://hdl.handle.net/1853/55646.

MLA Handbook (7th Edition):

Zou, Jun. “Social computing for personalization and credible information mining using probabilistic graphical models.” 2016. Web. 15 Jul 2019.

Vancouver:

Zou J. Social computing for personalization and credible information mining using probabilistic graphical models. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/1853/55646.

Council of Science Editors:

Zou J. Social computing for personalization and credible information mining using probabilistic graphical models. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55646

15. Yang, Shuang-Hong. Predictive models for online human activities.

Degree: PhD, Computing, 2012, Georgia Tech

 The availability and scale of user generated data in online systems raises tremendous challenges and opportunities to analytic study of human activities. Effective modeling of… (more)

Subjects/Keywords: Social contagion; Collaborative competitive filtering; Social ties; Behavior prediction; User-generated data; Redictive models; Online human activities; Language gap; User cognitive aspects; Content mining; Behavior-relation interplay; User-generated content; User interfaces (Computer systems); Data mining

…thesis is on behavior prediction. We present collaborative competitive filtering (CCF)… …introduction of collaborative filtering, the most popular techniques for personalization and… …These two will be used as foundations to the collaborative competitive filtering framework we… …mining user cognitive aspects, as will be presented in Chapter 7. 2.1 Collaborative filtering… …Collaborative filtering (CF) is widely used for establishing personalized systems such as… 

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APA (6th Edition):

Yang, S. (2012). Predictive models for online human activities. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43689

Chicago Manual of Style (16th Edition):

Yang, Shuang-Hong. “Predictive models for online human activities.” 2012. Doctoral Dissertation, Georgia Tech. Accessed July 15, 2019. http://hdl.handle.net/1853/43689.

MLA Handbook (7th Edition):

Yang, Shuang-Hong. “Predictive models for online human activities.” 2012. Web. 15 Jul 2019.

Vancouver:

Yang S. Predictive models for online human activities. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/1853/43689.

Council of Science Editors:

Yang S. Predictive models for online human activities. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43689

16. Parimi, Rohit. Collaborative filtering approaches for single-domain and cross-domain recommender systems.

Degree: PhD, Computing and Information Sciences, 2015, Kansas State University

 Increasing amounts of content on the Web means that users can select from a wide variety of items (i.e., items that concur with their tastes… (more)

Subjects/Keywords: Recommender systems; Collaborative filtering; Implicit feedback; Cross-domain; Adsorption; Matrix factorization; Computer Science (0984)

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APA (6th Edition):

Parimi, R. (2015). Collaborative filtering approaches for single-domain and cross-domain recommender systems. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/20108

Chicago Manual of Style (16th Edition):

Parimi, Rohit. “Collaborative filtering approaches for single-domain and cross-domain recommender systems.” 2015. Doctoral Dissertation, Kansas State University. Accessed July 15, 2019. http://hdl.handle.net/2097/20108.

MLA Handbook (7th Edition):

Parimi, Rohit. “Collaborative filtering approaches for single-domain and cross-domain recommender systems.” 2015. Web. 15 Jul 2019.

Vancouver:

Parimi R. Collaborative filtering approaches for single-domain and cross-domain recommender systems. [Internet] [Doctoral dissertation]. Kansas State University; 2015. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/2097/20108.

Council of Science Editors:

Parimi R. Collaborative filtering approaches for single-domain and cross-domain recommender systems. [Doctoral Dissertation]. Kansas State University; 2015. Available from: http://hdl.handle.net/2097/20108

17. Guillou, Frédéric. On recommendation systems in a sequential context : Des Systèmes de Recommandation dans un Contexte Séquentiel.

Degree: Docteur es, Informatique, 2016, Lille 3

Cette thèse porte sur l'étude des Systèmes de Recommandation dans un cadre séquentiel, où les retours des utilisateurs sur des articles arrivent dans le système… (more)

Subjects/Keywords: Systèmes de Recommandation; Recommandation Séquentielle; Filtrage Collaboratif; Factorisation de Matrice; Bandit Manchot; Feedback Séquentiel; Apprentissage de Classement; Recommendation Systems; Sequential Recommendation; Collaborative Filtering; Matrix Factorization; Multi-Armed Bandits; Sequential Feedback; Learning to Rank

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APA (6th Edition):

Guillou, F. (2016). On recommendation systems in a sequential context : Des Systèmes de Recommandation dans un Contexte Séquentiel. (Doctoral Dissertation). Lille 3. Retrieved from http://www.theses.fr/2016LIL30041

Chicago Manual of Style (16th Edition):

Guillou, Frédéric. “On recommendation systems in a sequential context : Des Systèmes de Recommandation dans un Contexte Séquentiel.” 2016. Doctoral Dissertation, Lille 3. Accessed July 15, 2019. http://www.theses.fr/2016LIL30041.

MLA Handbook (7th Edition):

Guillou, Frédéric. “On recommendation systems in a sequential context : Des Systèmes de Recommandation dans un Contexte Séquentiel.” 2016. Web. 15 Jul 2019.

Vancouver:

Guillou F. On recommendation systems in a sequential context : Des Systèmes de Recommandation dans un Contexte Séquentiel. [Internet] [Doctoral dissertation]. Lille 3; 2016. [cited 2019 Jul 15]. Available from: http://www.theses.fr/2016LIL30041.

Council of Science Editors:

Guillou F. On recommendation systems in a sequential context : Des Systèmes de Recommandation dans un Contexte Séquentiel. [Doctoral Dissertation]. Lille 3; 2016. Available from: http://www.theses.fr/2016LIL30041


Northeastern University

18. Shokat Fadaee, Saber. Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets.

Degree: PhD, Computer Science Program, 2017, Northeastern University

 Matrix representations are a natural way to represent many forms of networked and tabulated data. These include connections among people, user preferences over items, or… (more)

Subjects/Keywords: artificial Intelligence; collaborative filtering; deep learning; recommendation systems; social networks; statistical network modeling

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APA (6th Edition):

Shokat Fadaee, S. (2017). Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20284626

Chicago Manual of Style (16th Edition):

Shokat Fadaee, Saber. “Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets.” 2017. Doctoral Dissertation, Northeastern University. Accessed July 15, 2019. http://hdl.handle.net/2047/D20284626.

MLA Handbook (7th Edition):

Shokat Fadaee, Saber. “Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets.” 2017. Web. 15 Jul 2019.

Vancouver:

Shokat Fadaee S. Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets. [Internet] [Doctoral dissertation]. Northeastern University; 2017. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/2047/D20284626.

Council of Science Editors:

Shokat Fadaee S. Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets. [Doctoral Dissertation]. Northeastern University; 2017. Available from: http://hdl.handle.net/2047/D20284626


Oregon State University

19. Jung, Seikyung. Designing and understanding information retrieval systems using collaborative filtering in an academic library environment.

Degree: PhD, Computer Science, 2007, Oregon State University

 Accessing information on the Web has become ingrained into our daily lives, and we seek information from many different sources, including conference and journal publications,… (more)

Subjects/Keywords: Collaborative Filtering; Electronic information resource searching

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APA (6th Edition):

Jung, S. (2007). Designing and understanding information retrieval systems using collaborative filtering in an academic library environment. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/5694

Chicago Manual of Style (16th Edition):

Jung, Seikyung. “Designing and understanding information retrieval systems using collaborative filtering in an academic library environment.” 2007. Doctoral Dissertation, Oregon State University. Accessed July 15, 2019. http://hdl.handle.net/1957/5694.

MLA Handbook (7th Edition):

Jung, Seikyung. “Designing and understanding information retrieval systems using collaborative filtering in an academic library environment.” 2007. Web. 15 Jul 2019.

Vancouver:

Jung S. Designing and understanding information retrieval systems using collaborative filtering in an academic library environment. [Internet] [Doctoral dissertation]. Oregon State University; 2007. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/1957/5694.

Council of Science Editors:

Jung S. Designing and understanding information retrieval systems using collaborative filtering in an academic library environment. [Doctoral Dissertation]. Oregon State University; 2007. Available from: http://hdl.handle.net/1957/5694

20. Pozo, Manuel. Towards Accurate and Scalable Recommender Systems : Contributions à l'efficacité et au passage à l'échelle des Systèmes de Recommandations.

Degree: Docteur es, Informatique, 2016, Paris, CNAM

Les systèmes de recommandation visent à présélectionner et présenter en premier les informations susceptibles d'intéresser les utilisateurs. Ceci a suscité l'attention du commerce électronique, où… (more)

Subjects/Keywords: Filtrage collaboratif; Système de recommandation; Distribution; Filtre de bloom; Demarrage à froid; Apprentissage actif; Collaborative filtering; Recommender system; Distribution; Bloom filter; Cold-Start; Active learning; 004.019

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APA (6th Edition):

Pozo, M. (2016). Towards Accurate and Scalable Recommender Systems : Contributions à l'efficacité et au passage à l'échelle des Systèmes de Recommandations. (Doctoral Dissertation). Paris, CNAM. Retrieved from http://www.theses.fr/2016CNAM1061

Chicago Manual of Style (16th Edition):

Pozo, Manuel. “Towards Accurate and Scalable Recommender Systems : Contributions à l'efficacité et au passage à l'échelle des Systèmes de Recommandations.” 2016. Doctoral Dissertation, Paris, CNAM. Accessed July 15, 2019. http://www.theses.fr/2016CNAM1061.

MLA Handbook (7th Edition):

Pozo, Manuel. “Towards Accurate and Scalable Recommender Systems : Contributions à l'efficacité et au passage à l'échelle des Systèmes de Recommandations.” 2016. Web. 15 Jul 2019.

Vancouver:

Pozo M. Towards Accurate and Scalable Recommender Systems : Contributions à l'efficacité et au passage à l'échelle des Systèmes de Recommandations. [Internet] [Doctoral dissertation]. Paris, CNAM; 2016. [cited 2019 Jul 15]. Available from: http://www.theses.fr/2016CNAM1061.

Council of Science Editors:

Pozo M. Towards Accurate and Scalable Recommender Systems : Contributions à l'efficacité et au passage à l'échelle des Systèmes de Recommandations. [Doctoral Dissertation]. Paris, CNAM; 2016. Available from: http://www.theses.fr/2016CNAM1061

21. Schmitt, Thomas. Appariements collaboratifs des offres et demandes d’emploi : Collaborative Matching of Job Openings and Job Seekers.

Degree: Docteur es, Informatique, 2018, Paris Saclay

Notre recherche porte sur la recommandation de nouvelles offres d'emploi venant d'être postées et n'ayant pas d'historique d'interactions (démarrage à froid). Nous adaptons les systèmes… (more)

Subjects/Keywords: Filtrage collaborative; Système de recommandation; Apprentissage de métrique; Réseaux dde neurones; Traitement des lagues naturelles; Collaborative filtering; Metric learning; Recommender system; Natural language processing; Neutral network

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APA (6th Edition):

Schmitt, T. (2018). Appariements collaboratifs des offres et demandes d’emploi : Collaborative Matching of Job Openings and Job Seekers. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2018SACLS210

Chicago Manual of Style (16th Edition):

Schmitt, Thomas. “Appariements collaboratifs des offres et demandes d’emploi : Collaborative Matching of Job Openings and Job Seekers.” 2018. Doctoral Dissertation, Paris Saclay. Accessed July 15, 2019. http://www.theses.fr/2018SACLS210.

MLA Handbook (7th Edition):

Schmitt, Thomas. “Appariements collaboratifs des offres et demandes d’emploi : Collaborative Matching of Job Openings and Job Seekers.” 2018. Web. 15 Jul 2019.

Vancouver:

Schmitt T. Appariements collaboratifs des offres et demandes d’emploi : Collaborative Matching of Job Openings and Job Seekers. [Internet] [Doctoral dissertation]. Paris Saclay; 2018. [cited 2019 Jul 15]. Available from: http://www.theses.fr/2018SACLS210.

Council of Science Editors:

Schmitt T. Appariements collaboratifs des offres et demandes d’emploi : Collaborative Matching of Job Openings and Job Seekers. [Doctoral Dissertation]. Paris Saclay; 2018. Available from: http://www.theses.fr/2018SACLS210

22. Zhang, Zhuo. Sparsity, robustness, and diversification of Recommender Systems .

Degree: PhD, 2014, Princeton University

 Recommender systems have played an important role in helping individuals select useful items or places of interest when they face too many choices. Collaborative filtering(more)

Subjects/Keywords: Collaborative Filtering; Diversification; Recommender System; Robustness; Sparisity

…By analyzing the available ratings, collaborative filtering attempts to make the best… …rating matrix 1 is very sparse. The traditional collaborative filtering algorithms will… …available information is one of the trends in the future. 1.1 Iterative Collaborative Filtering… …in Sparse Recommender Systems Collaborative filtering (CF) is one of the most… …methods. 1.2 Shilling Attack Detection using Graph-based Algorithms Collaborative filtering… 

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APA (6th Edition):

Zhang, Z. (2014). Sparsity, robustness, and diversification of Recommender Systems . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01g732dc22c

Chicago Manual of Style (16th Edition):

Zhang, Zhuo. “Sparsity, robustness, and diversification of Recommender Systems .” 2014. Doctoral Dissertation, Princeton University. Accessed July 15, 2019. http://arks.princeton.edu/ark:/88435/dsp01g732dc22c.

MLA Handbook (7th Edition):

Zhang, Zhuo. “Sparsity, robustness, and diversification of Recommender Systems .” 2014. Web. 15 Jul 2019.

Vancouver:

Zhang Z. Sparsity, robustness, and diversification of Recommender Systems . [Internet] [Doctoral dissertation]. Princeton University; 2014. [cited 2019 Jul 15]. Available from: http://arks.princeton.edu/ark:/88435/dsp01g732dc22c.

Council of Science Editors:

Zhang Z. Sparsity, robustness, and diversification of Recommender Systems . [Doctoral Dissertation]. Princeton University; 2014. Available from: http://arks.princeton.edu/ark:/88435/dsp01g732dc22c

23. Salah, Aghiles. Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data : Modèles de mélange de von Mises-Fisher pour la classification simple et croisée de données éparses de grande dimension.

Degree: Docteur es, Science de données, 2016, Sorbonne Paris Cité

 La classification automatique, qui consiste à regrouper des objets similaires au sein de groupes, également appelés classes ou clusters, est sans aucun doute l’une des… (more)

Subjects/Keywords: Apprentissage statistique; Classification; Classification croisée; Modèles de mélanges; Statistiques directionnelles; Distribution de von Mises-Fisher; Fouille de textes; Systèmes de recommandation; Filtrage collaboratif; Matrices creuses; Grande dimension; Machine learning; Clustering; Co-clustering; Mixture models; Directional statistics; Von Mises-Fisher distribution; Text mining; Recommender systems; Collaborative filtering; Sparse data; High dimensional data; 003.3

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APA (6th Edition):

Salah, A. (2016). Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data : Modèles de mélange de von Mises-Fisher pour la classification simple et croisée de données éparses de grande dimension. (Doctoral Dissertation). Sorbonne Paris Cité. Retrieved from http://www.theses.fr/2016USPCB093

Chicago Manual of Style (16th Edition):

Salah, Aghiles. “Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data : Modèles de mélange de von Mises-Fisher pour la classification simple et croisée de données éparses de grande dimension.” 2016. Doctoral Dissertation, Sorbonne Paris Cité. Accessed July 15, 2019. http://www.theses.fr/2016USPCB093.

MLA Handbook (7th Edition):

Salah, Aghiles. “Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data : Modèles de mélange de von Mises-Fisher pour la classification simple et croisée de données éparses de grande dimension.” 2016. Web. 15 Jul 2019.

Vancouver:

Salah A. Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data : Modèles de mélange de von Mises-Fisher pour la classification simple et croisée de données éparses de grande dimension. [Internet] [Doctoral dissertation]. Sorbonne Paris Cité; 2016. [cited 2019 Jul 15]. Available from: http://www.theses.fr/2016USPCB093.

Council of Science Editors:

Salah A. Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data : Modèles de mélange de von Mises-Fisher pour la classification simple et croisée de données éparses de grande dimension. [Doctoral Dissertation]. Sorbonne Paris Cité; 2016. Available from: http://www.theses.fr/2016USPCB093

24. Chamsi Abu Quba, Rana. On enhancing recommender systems by utilizing general social networks combined with users goals and contextual awareness : Renforcement des systèmes de recommandation à l'aide de réseaux sociaux et en combinant les objectifs et les préférences des usagers et la prise en compte du contexte.

Degree: Docteur es, Informatique, 2015, Université Claude Bernard – Lyon I

Nous sommes amenés chaque jour à prendre un nombre important de décisions : quel nouveau livre lire ? Quel film regarder ce soir ou où… (more)

Subjects/Keywords: Réseau social; Système de recommandation; Profil de l'utilisateur; Filtrage collaboratif; Social network; Recommendation system; User profile; Collaborative filtering; 004.69

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APA (6th Edition):

Chamsi Abu Quba, R. (2015). On enhancing recommender systems by utilizing general social networks combined with users goals and contextual awareness : Renforcement des systèmes de recommandation à l'aide de réseaux sociaux et en combinant les objectifs et les préférences des usagers et la prise en compte du contexte. (Doctoral Dissertation). Université Claude Bernard – Lyon I. Retrieved from http://www.theses.fr/2015LYO10061

Chicago Manual of Style (16th Edition):

Chamsi Abu Quba, Rana. “On enhancing recommender systems by utilizing general social networks combined with users goals and contextual awareness : Renforcement des systèmes de recommandation à l'aide de réseaux sociaux et en combinant les objectifs et les préférences des usagers et la prise en compte du contexte.” 2015. Doctoral Dissertation, Université Claude Bernard – Lyon I. Accessed July 15, 2019. http://www.theses.fr/2015LYO10061.

MLA Handbook (7th Edition):

Chamsi Abu Quba, Rana. “On enhancing recommender systems by utilizing general social networks combined with users goals and contextual awareness : Renforcement des systèmes de recommandation à l'aide de réseaux sociaux et en combinant les objectifs et les préférences des usagers et la prise en compte du contexte.” 2015. Web. 15 Jul 2019.

Vancouver:

Chamsi Abu Quba R. On enhancing recommender systems by utilizing general social networks combined with users goals and contextual awareness : Renforcement des systèmes de recommandation à l'aide de réseaux sociaux et en combinant les objectifs et les préférences des usagers et la prise en compte du contexte. [Internet] [Doctoral dissertation]. Université Claude Bernard – Lyon I; 2015. [cited 2019 Jul 15]. Available from: http://www.theses.fr/2015LYO10061.

Council of Science Editors:

Chamsi Abu Quba R. On enhancing recommender systems by utilizing general social networks combined with users goals and contextual awareness : Renforcement des systèmes de recommandation à l'aide de réseaux sociaux et en combinant les objectifs et les préférences des usagers et la prise en compte du contexte. [Doctoral Dissertation]. Université Claude Bernard – Lyon I; 2015. Available from: http://www.theses.fr/2015LYO10061


Université de Grenoble

25. Meyer, Frank. Systèmes de recommandation dans des contextes industriels : Recommender systems in industrial contexts.

Degree: Docteur es, Informatique, 2012, Université de Grenoble

Cette thèse traite des systèmes de recommandation automatiques. Les moteurs de recommandation automatique sont des systèmes qui permettent, par des techniques de data mining, de… (more)

Subjects/Keywords: Filtrage collaboratif; Filtrage par le contenu; Système de recommandation; Contexte industriel; Evaluation des systèmes de recommandation; Hybridation switch-based; Collaborative Filtering; Content based filtering; Système de recommandation; Industrial context; Evaluation of recommender systems; Switch-based hybrid systems

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APA (6th Edition):

Meyer, F. (2012). Systèmes de recommandation dans des contextes industriels : Recommender systems in industrial contexts. (Doctoral Dissertation). Université de Grenoble. Retrieved from http://www.theses.fr/2012GRENM004

Chicago Manual of Style (16th Edition):

Meyer, Frank. “Systèmes de recommandation dans des contextes industriels : Recommender systems in industrial contexts.” 2012. Doctoral Dissertation, Université de Grenoble. Accessed July 15, 2019. http://www.theses.fr/2012GRENM004.

MLA Handbook (7th Edition):

Meyer, Frank. “Systèmes de recommandation dans des contextes industriels : Recommender systems in industrial contexts.” 2012. Web. 15 Jul 2019.

Vancouver:

Meyer F. Systèmes de recommandation dans des contextes industriels : Recommender systems in industrial contexts. [Internet] [Doctoral dissertation]. Université de Grenoble; 2012. [cited 2019 Jul 15]. Available from: http://www.theses.fr/2012GRENM004.

Council of Science Editors:

Meyer F. Systèmes de recommandation dans des contextes industriels : Recommender systems in industrial contexts. [Doctoral Dissertation]. Université de Grenoble; 2012. Available from: http://www.theses.fr/2012GRENM004

26. Ben Ticha, Sonia. Recommandation personnalisée hybride : Hybrid personalized recommendation.

Degree: Docteur es, Informatique, 2015, Université de Lorraine; Université de Tunis El Manar

Face à la surabondance des ressources et de l'information sur le net, l'accès aux ressources pertinentes devient une tâche fastidieuse pour les usagers de la… (more)

Subjects/Keywords: Recommandation personnalisée; Filtrage collaboratif; Contenu des ressources; Profil sémantique de l’utilisateur; Personalized recommendation; Collaborative filtering; Item content; User semantic profile; 004.678

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APA (6th Edition):

Ben Ticha, S. (2015). Recommandation personnalisée hybride : Hybrid personalized recommendation. (Doctoral Dissertation). Université de Lorraine; Université de Tunis El Manar. Retrieved from http://www.theses.fr/2015LORR0168

Chicago Manual of Style (16th Edition):

Ben Ticha, Sonia. “Recommandation personnalisée hybride : Hybrid personalized recommendation.” 2015. Doctoral Dissertation, Université de Lorraine; Université de Tunis El Manar. Accessed July 15, 2019. http://www.theses.fr/2015LORR0168.

MLA Handbook (7th Edition):

Ben Ticha, Sonia. “Recommandation personnalisée hybride : Hybrid personalized recommendation.” 2015. Web. 15 Jul 2019.

Vancouver:

Ben Ticha S. Recommandation personnalisée hybride : Hybrid personalized recommendation. [Internet] [Doctoral dissertation]. Université de Lorraine; Université de Tunis El Manar; 2015. [cited 2019 Jul 15]. Available from: http://www.theses.fr/2015LORR0168.

Council of Science Editors:

Ben Ticha S. Recommandation personnalisée hybride : Hybrid personalized recommendation. [Doctoral Dissertation]. Université de Lorraine; Université de Tunis El Manar; 2015. Available from: http://www.theses.fr/2015LORR0168


University of Cincinnati

27. Strunjas, Svetlana. Algorithms and Models for Collaborative Filtering from Large Information Corpora.

Degree: PhD, Engineering : Computer Science, 2008, University of Cincinnati

  In this thesis we propose novel collaborative filtering approaches for large data sets. We also demonstrate how these collaborative approaches can be used for… (more)

Subjects/Keywords: Computer Science; collaborative filtering; collaborative partitioning; clustering; information retrieval

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

APA (6th Edition):

Strunjas, S. (2008). Algorithms and Models for Collaborative Filtering from Large Information Corpora. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1220001182

Chicago Manual of Style (16th Edition):

Strunjas, Svetlana. “Algorithms and Models for Collaborative Filtering from Large Information Corpora.” 2008. Doctoral Dissertation, University of Cincinnati. Accessed July 15, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1220001182.

MLA Handbook (7th Edition):

Strunjas, Svetlana. “Algorithms and Models for Collaborative Filtering from Large Information Corpora.” 2008. Web. 15 Jul 2019.

Vancouver:

Strunjas S. Algorithms and Models for Collaborative Filtering from Large Information Corpora. [Internet] [Doctoral dissertation]. University of Cincinnati; 2008. [cited 2019 Jul 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1220001182.

Council of Science Editors:

Strunjas S. Algorithms and Models for Collaborative Filtering from Large Information Corpora. [Doctoral Dissertation]. University of Cincinnati; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1220001182


University of Colorado

28. Gartrell, Charles Michael. Enhancing Recommender Systems Using Social Indicators.

Degree: PhD, Computer Science, 2014, University of Colorado

  Recommender systems are increasingly driving user experiences on the Internet. In recent years, online social networks have quickly become the fastest growing part of… (more)

Subjects/Keywords: collaborative filtering; group recommendation; machine learning; mobile computing; recommender systems; social networks; Computer Sciences

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APA (6th Edition):

Gartrell, C. M. (2014). Enhancing Recommender Systems Using Social Indicators. (Doctoral Dissertation). University of Colorado. Retrieved from http://scholar.colorado.edu/csci_gradetds/3

Chicago Manual of Style (16th Edition):

Gartrell, Charles Michael. “Enhancing Recommender Systems Using Social Indicators.” 2014. Doctoral Dissertation, University of Colorado. Accessed July 15, 2019. http://scholar.colorado.edu/csci_gradetds/3.

MLA Handbook (7th Edition):

Gartrell, Charles Michael. “Enhancing Recommender Systems Using Social Indicators.” 2014. Web. 15 Jul 2019.

Vancouver:

Gartrell CM. Enhancing Recommender Systems Using Social Indicators. [Internet] [Doctoral dissertation]. University of Colorado; 2014. [cited 2019 Jul 15]. Available from: http://scholar.colorado.edu/csci_gradetds/3.

Council of Science Editors:

Gartrell CM. Enhancing Recommender Systems Using Social Indicators. [Doctoral Dissertation]. University of Colorado; 2014. Available from: http://scholar.colorado.edu/csci_gradetds/3

29. Hassanzadeh, Farzad. Distances on rankings: from social choice to flash memories.

Degree: PhD, 1200, 2013, University of Illinois – Urbana-Champaign

 From social choice to statistics to coding theory, rankings are found to be a useful vehicle for storing and presenting information in modern data systems.… (more)

Subjects/Keywords: Distance; Rankings; Permutations; Social choice; Flash memories; Kendall tau distance; Weighted Kendall distance; Weighted Transposition distance; Rank aggregation; Information Retrieval; Collaborative filtering; Rank modulation; Ulam distance; error-correcting codes

collaborative filtering, if user preferences are presented as rankings, distance measures between… …addition to rank aggregation, weighted distances are useful for collaborative filtering [18… …agents [17] as well as in recommender systems in the context of collaborative… …filtering [18]. In coding theory, transmitting rankings instead of absolute values was… 

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

APA (6th Edition):

Hassanzadeh, F. (2013). Distances on rankings: from social choice to flash memories. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/44268

Chicago Manual of Style (16th Edition):

Hassanzadeh, Farzad. “Distances on rankings: from social choice to flash memories.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed July 15, 2019. http://hdl.handle.net/2142/44268.

MLA Handbook (7th Edition):

Hassanzadeh, Farzad. “Distances on rankings: from social choice to flash memories.” 2013. Web. 15 Jul 2019.

Vancouver:

Hassanzadeh F. Distances on rankings: from social choice to flash memories. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/2142/44268.

Council of Science Editors:

Hassanzadeh F. Distances on rankings: from social choice to flash memories. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/44268


University of Kansas

30. Cinicioglu, Esma N. On Solving Stochastic PERT Networks and Using RFIDs for Operations Management.

Degree: PH.D., Business, 2008, University of Kansas

 The current methods used to solve stochastic PERT networks overlook the true distribution of the maximum of two distributions and thus fail to compute an… (more)

Subjects/Keywords: Business administration; Pert; Bayesian networks; Mixtures of exponentials; Rfid; Targeted advertising; Collaborative filtering; http://id.worldcat.org/fast/536264; http://id.worldcat.org/fast/1049839; United States. Small Business Administration; PERT (Network analysis)

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

APA (6th Edition):

Cinicioglu, E. N. (2008). On Solving Stochastic PERT Networks and Using RFIDs for Operations Management. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/4314

Chicago Manual of Style (16th Edition):

Cinicioglu, Esma N. “On Solving Stochastic PERT Networks and Using RFIDs for Operations Management.” 2008. Doctoral Dissertation, University of Kansas. Accessed July 15, 2019. http://hdl.handle.net/1808/4314.

MLA Handbook (7th Edition):

Cinicioglu, Esma N. “On Solving Stochastic PERT Networks and Using RFIDs for Operations Management.” 2008. Web. 15 Jul 2019.

Vancouver:

Cinicioglu EN. On Solving Stochastic PERT Networks and Using RFIDs for Operations Management. [Internet] [Doctoral dissertation]. University of Kansas; 2008. [cited 2019 Jul 15]. Available from: http://hdl.handle.net/1808/4314.

Council of Science Editors:

Cinicioglu EN. On Solving Stochastic PERT Networks and Using RFIDs for Operations Management. [Doctoral Dissertation]. University of Kansas; 2008. Available from: http://hdl.handle.net/1808/4314

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