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You searched for subject:(Collaborative filtering). Showing records 31 – 60 of 207 total matches.

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Victoria University of Wellington

31. Zhang, Yun. Exploiting Latent Information in Recommender Systems.

Degree: 2012, Victoria University of Wellington

 This thesis exploits latent information in personalised recommendation, and investigates how this information can be used to improve recommender systems. The investigations span three directions:… (more)

Subjects/Keywords: Recommender system; Collaborative filtering; Data mining; Machine learning; Information retrieval

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APA (6th Edition):

Zhang, Y. (2012). Exploiting Latent Information in Recommender Systems. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/2455

Chicago Manual of Style (16th Edition):

Zhang, Yun. “Exploiting Latent Information in Recommender Systems.” 2012. Doctoral Dissertation, Victoria University of Wellington. Accessed June 16, 2019. http://hdl.handle.net/10063/2455.

MLA Handbook (7th Edition):

Zhang, Yun. “Exploiting Latent Information in Recommender Systems.” 2012. Web. 16 Jun 2019.

Vancouver:

Zhang Y. Exploiting Latent Information in Recommender Systems. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2012. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10063/2455.

Council of Science Editors:

Zhang Y. Exploiting Latent Information in Recommender Systems. [Doctoral Dissertation]. Victoria University of Wellington; 2012. Available from: http://hdl.handle.net/10063/2455


University of Georgia

32. 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 June 16, 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. 16 Jun 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 Jun 16]. 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


Georgia Tech

33. 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 June 16, 2019. http://hdl.handle.net/1853/53851.

MLA Handbook (7th Edition):

Yu, Hong. “A data-driven approach for personalized drama management.” 2015. Web. 16 Jun 2019.

Vancouver:

Yu H. A data-driven approach for personalized drama management. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Jun 16]. 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

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

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 June 16, 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. 16 Jun 2019.

Vancouver:

Zou J. Social computing for personalization and credible information mining using probabilistic graphical models. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Jun 16]. 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

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

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 June 16, 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. 16 Jun 2019.

Vancouver:

Κουτσόπουλος . Ανάπτυξη εφαρμογής συνεργατικών συστάσεων βασισμένη σε οντολογίες για κινητές εμπορικές υπηρεσίες. [Internet] [Masters thesis]. University of Patras; 2014. [cited 2019 Jun 16]. 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


Mid Sweden University

36. Flodin, Anton. Leerec : A scalable product recommendation engine suitable for transaction data.

Degree: Information Systems and Technology, 2018, Mid Sweden University

  We are currently living in the Internet of Things (IoT) era, which involves devices that are connected to Internet and are communicating with each… (more)

Subjects/Keywords: Collaborative filtering; log processing; event; Alternating Least Square; Computer Systems; Datorsystem

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APA (6th Edition):

Flodin, A. (2018). Leerec : A scalable product recommendation engine suitable for transaction data. (Thesis). Mid Sweden University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33941

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Flodin, Anton. “Leerec : A scalable product recommendation engine suitable for transaction data.” 2018. Thesis, Mid Sweden University. Accessed June 16, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33941.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Flodin, Anton. “Leerec : A scalable product recommendation engine suitable for transaction data.” 2018. Web. 16 Jun 2019.

Vancouver:

Flodin A. Leerec : A scalable product recommendation engine suitable for transaction data. [Internet] [Thesis]. Mid Sweden University; 2018. [cited 2019 Jun 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33941.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Flodin A. Leerec : A scalable product recommendation engine suitable for transaction data. [Thesis]. Mid Sweden University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33941

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of California – Riverside

37. Elsisy, Amr. Exploring Temporal Context for Collaborative Filtering.

Degree: Computer Science, 2017, University of California – Riverside

 Thousands of new users join social media website everyday, generating huge amounts of new data. Twitter users for example, generate millions of new posts per… (more)

Subjects/Keywords: Computer science; Collaborative; Context; Filtering; Recommendation; Systems; Temporal

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APA (6th Edition):

Elsisy, A. (2017). Exploring Temporal Context for Collaborative Filtering. (Thesis). University of California – Riverside. Retrieved from http://www.escholarship.org/uc/item/3dx874qn

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Elsisy, Amr. “Exploring Temporal Context for Collaborative Filtering.” 2017. Thesis, University of California – Riverside. Accessed June 16, 2019. http://www.escholarship.org/uc/item/3dx874qn.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Elsisy, Amr. “Exploring Temporal Context for Collaborative Filtering.” 2017. Web. 16 Jun 2019.

Vancouver:

Elsisy A. Exploring Temporal Context for Collaborative Filtering. [Internet] [Thesis]. University of California – Riverside; 2017. [cited 2019 Jun 16]. Available from: http://www.escholarship.org/uc/item/3dx874qn.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Elsisy A. Exploring Temporal Context for Collaborative Filtering. [Thesis]. University of California – Riverside; 2017. Available from: http://www.escholarship.org/uc/item/3dx874qn

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


UCLA

38. Hu, Chufeng. Application of Neural Network Based Recommendation System.

Degree: Statistics, 2017, UCLA

 In the recommendation system, data comes in the form of a vector or matrix. Matrix factorization techniques attempt to recover missing or corrupted entries by… (more)

Subjects/Keywords: Statistics; collaborative filtering; matrix factorization; neural network; recommendation system

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

APA (6th Edition):

Hu, C. (2017). Application of Neural Network Based Recommendation System. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/4dc4v66k

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hu, Chufeng. “Application of Neural Network Based Recommendation System.” 2017. Thesis, UCLA. Accessed June 16, 2019. http://www.escholarship.org/uc/item/4dc4v66k.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hu, Chufeng. “Application of Neural Network Based Recommendation System.” 2017. Web. 16 Jun 2019.

Vancouver:

Hu C. Application of Neural Network Based Recommendation System. [Internet] [Thesis]. UCLA; 2017. [cited 2019 Jun 16]. Available from: http://www.escholarship.org/uc/item/4dc4v66k.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hu C. Application of Neural Network Based Recommendation System. [Thesis]. UCLA; 2017. Available from: http://www.escholarship.org/uc/item/4dc4v66k

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Saskatchewan

39. Alotaibi, Shaikhah N. IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS.

Degree: 2016, University of Saskatchewan

 Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area… (more)

Subjects/Keywords: recommender system; collaborative filtering; social recommendation; user coverage; diversity

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APA (6th Edition):

Alotaibi, S. N. (2016). IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/7457

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Alotaibi, Shaikhah N. “IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS.” 2016. Thesis, University of Saskatchewan. Accessed June 16, 2019. http://hdl.handle.net/10388/7457.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Alotaibi, Shaikhah N. “IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS.” 2016. Web. 16 Jun 2019.

Vancouver:

Alotaibi SN. IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS. [Internet] [Thesis]. University of Saskatchewan; 2016. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10388/7457.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alotaibi SN. IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS. [Thesis]. University of Saskatchewan; 2016. Available from: http://hdl.handle.net/10388/7457

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University College Cork

40. Griffith, Josephine. An analysis of collaborative filtering datasets.

Degree: 2018, University College Cork

 The work described in this thesis pertains to the area of Collaborative Filtering and focuses on collaborative filtering datasets and specially-defined portions of the datasets… (more)

Subjects/Keywords: Collaborative filtering; Dataset views; Performance prediction; Machine learning

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APA (6th Edition):

Griffith, J. (2018). An analysis of collaborative filtering datasets. (Thesis). University College Cork. Retrieved from http://hdl.handle.net/10468/5532

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Griffith, Josephine. “An analysis of collaborative filtering datasets.” 2018. Thesis, University College Cork. Accessed June 16, 2019. http://hdl.handle.net/10468/5532.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Griffith, Josephine. “An analysis of collaborative filtering datasets.” 2018. Web. 16 Jun 2019.

Vancouver:

Griffith J. An analysis of collaborative filtering datasets. [Internet] [Thesis]. University College Cork; 2018. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10468/5532.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Griffith J. An analysis of collaborative filtering datasets. [Thesis]. University College Cork; 2018. Available from: http://hdl.handle.net/10468/5532

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universidade Nova

41. Dias, Pedro Ricardo Gomes. Recommending media content based on machine learning methods.

Degree: 2011, Universidade Nova

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Information is nowadays made available and consumed faster than ever before. This information technology generation… (more)

Subjects/Keywords: Recommender systems; Collaborative filtering; Matrix factorization; Groupbased recommendations; Interactive TV

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APA (6th Edition):

Dias, P. R. G. (2011). Recommending media content based on machine learning methods. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/6581

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Dias, Pedro Ricardo Gomes. “Recommending media content based on machine learning methods.” 2011. Thesis, Universidade Nova. Accessed June 16, 2019. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/6581.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Dias, Pedro Ricardo Gomes. “Recommending media content based on machine learning methods.” 2011. Web. 16 Jun 2019.

Vancouver:

Dias PRG. Recommending media content based on machine learning methods. [Internet] [Thesis]. Universidade Nova; 2011. [cited 2019 Jun 16]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/6581.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dias PRG. Recommending media content based on machine learning methods. [Thesis]. Universidade Nova; 2011. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/6581

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


KTH

42. Shahan, Michel. Personalized TV with Recommendations : Integrating Social Networks.

Degree: CoS, 2008, KTH

This master’s thesis concerns how to recommend multimedia content which a user might view – with some media player. It describes how a computer… (more)

Subjects/Keywords: Collaborative Filtering; Recommendations; Recommendation Engines; Social Networks; Trust; Correlation; TECHNOLOGY; TEKNIKVETENSKAP

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

APA (6th Edition):

Shahan, M. (2008). Personalized TV with Recommendations : Integrating Social Networks. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91670

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Shahan, Michel. “Personalized TV with Recommendations : Integrating Social Networks.” 2008. Thesis, KTH. Accessed June 16, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91670.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Shahan, Michel. “Personalized TV with Recommendations : Integrating Social Networks.” 2008. Web. 16 Jun 2019.

Vancouver:

Shahan M. Personalized TV with Recommendations : Integrating Social Networks. [Internet] [Thesis]. KTH; 2008. [cited 2019 Jun 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91670.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shahan M. Personalized TV with Recommendations : Integrating Social Networks. [Thesis]. KTH; 2008. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91670

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

43. CARRA, Florian. Recommender System for Retail Industry : Ease customers’ purchase by generating personal purchase carts consisting of relevant and original products.

Degree: Computer Science and Communication (CSC), 2016, KTH

  In this study we explore the problem of purchase cart recommendationin the field of retail. How can we push the right customize purchase cart… (more)

Subjects/Keywords: Collaborative Filtering; Recommender System

Filtering, Collaborative Filtering & Advanced Collaborative Filtering. Last point being tackled in… …account, as opposed to Collaborative Filtering where the model is built for everyone) and… …Collaborative Filtering. Collaborative Filtering, as opposed to Content-Based Filtering, uses… …Item Matrix Notion of user-item matrix is common to many Collaborative Filtering methods… …computed similarity measure would be high 19 Chapter 5 Advanced Collaborative Filtering… 

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APA (6th Edition):

CARRA, F. (2016). Recommender System for Retail Industry : Ease customers’ purchase by generating personal purchase carts consisting of relevant and original products. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181907

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

CARRA, Florian. “Recommender System for Retail Industry : Ease customers’ purchase by generating personal purchase carts consisting of relevant and original products.” 2016. Thesis, KTH. Accessed June 16, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181907.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

CARRA, Florian. “Recommender System for Retail Industry : Ease customers’ purchase by generating personal purchase carts consisting of relevant and original products.” 2016. Web. 16 Jun 2019.

Vancouver:

CARRA F. Recommender System for Retail Industry : Ease customers’ purchase by generating personal purchase carts consisting of relevant and original products. [Internet] [Thesis]. KTH; 2016. [cited 2019 Jun 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181907.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

CARRA F. Recommender System for Retail Industry : Ease customers’ purchase by generating personal purchase carts consisting of relevant and original products. [Thesis]. KTH; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181907

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rochester Institute of Technology

44. 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 June 16, 2019. https://scholarworks.rit.edu/theses/9461.

MLA Handbook (7th Edition):

Matus Nicodemos, Marcelo. “Information-Based Neighborhood Modeling.” 2017. Web. 16 Jun 2019.

Vancouver:

Matus Nicodemos M. Information-Based Neighborhood Modeling. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2019 Jun 16]. 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


University of Notre Dame

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

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 June 16, 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. 16 Jun 2019.

Vancouver:

Davis DA. Predicting Individual Disease Risk Based on Medical History</h1>. [Internet] [Masters thesis]. University of Notre Dame; 2008. [cited 2019 Jun 16]. 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


University of New South Wales

46. Chowdhury, Nipa. Collaborative learning for recommender systems by boosting and non-parametric methods.

Degree: Computer Science & Engineering, 2016, University of New South Wales

 As the Internet becomes larger in size, its information content threatens to be-come overwhelming. Therefore, recommender systems have gained much attentionin information retrieval to guide… (more)

Subjects/Keywords: Matrix Factorisation,; Recommender Systems,; Collaborative Filtering,; Boosting,; Non-parametric methods

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

APA (6th Edition):

Chowdhury, N. (2016). Collaborative learning for recommender systems by boosting and non-parametric methods. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/58816 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47632/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Chowdhury, Nipa. “Collaborative learning for recommender systems by boosting and non-parametric methods.” 2016. Doctoral Dissertation, University of New South Wales. Accessed June 16, 2019. http://handle.unsw.edu.au/1959.4/58816 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47632/SOURCE02?view=true.

MLA Handbook (7th Edition):

Chowdhury, Nipa. “Collaborative learning for recommender systems by boosting and non-parametric methods.” 2016. Web. 16 Jun 2019.

Vancouver:

Chowdhury N. Collaborative learning for recommender systems by boosting and non-parametric methods. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2019 Jun 16]. Available from: http://handle.unsw.edu.au/1959.4/58816 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47632/SOURCE02?view=true.

Council of Science Editors:

Chowdhury N. Collaborative learning for recommender systems by boosting and non-parametric methods. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/58816 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47632/SOURCE02?view=true


University of Kentucky

47. Alghamedy, Fatemah. ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION.

Degree: 2019, University of Kentucky

 This dissertation studies the factors that negatively impact the accuracy of the collaborative filtering recommendation systems based on nonnegative matrix factorization (NMF). The keystone in… (more)

Subjects/Keywords: recommendation system; collaborative filtering; NMF; trust matrix; imputation; Computer Sciences

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

APA (6th Edition):

Alghamedy, F. (2019). ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION. (Doctoral Dissertation). University of Kentucky. Retrieved from https://uknowledge.uky.edu/cs_etds/79

Chicago Manual of Style (16th Edition):

Alghamedy, Fatemah. “ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION.” 2019. Doctoral Dissertation, University of Kentucky. Accessed June 16, 2019. https://uknowledge.uky.edu/cs_etds/79.

MLA Handbook (7th Edition):

Alghamedy, Fatemah. “ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION.” 2019. Web. 16 Jun 2019.

Vancouver:

Alghamedy F. ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION. [Internet] [Doctoral dissertation]. University of Kentucky; 2019. [cited 2019 Jun 16]. Available from: https://uknowledge.uky.edu/cs_etds/79.

Council of Science Editors:

Alghamedy F. ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION. [Doctoral Dissertation]. University of Kentucky; 2019. Available from: https://uknowledge.uky.edu/cs_etds/79


NSYSU

48. Huang, Hsin-Chieh. A Content via Collaboration Approach to Text Filtering Recommender Systems.

Degree: Master, Information Management, 2006, NSYSU

 Ever since the rapid growth of the Internet, recommender systems have become essential in helping online users to search and retrieve relevant information they need.… (more)

Subjects/Keywords: recommender systems; collaborative filtering; content-based filtering; LSI

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

APA (6th Edition):

Huang, H. (2006). A Content via Collaboration Approach to Text Filtering Recommender Systems. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0801106-224333

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Huang, Hsin-Chieh. “A Content via Collaboration Approach to Text Filtering Recommender Systems.” 2006. Thesis, NSYSU. Accessed June 16, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0801106-224333.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Huang, Hsin-Chieh. “A Content via Collaboration Approach to Text Filtering Recommender Systems.” 2006. Web. 16 Jun 2019.

Vancouver:

Huang H. A Content via Collaboration Approach to Text Filtering Recommender Systems. [Internet] [Thesis]. NSYSU; 2006. [cited 2019 Jun 16]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0801106-224333.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huang H. A Content via Collaboration Approach to Text Filtering Recommender Systems. [Thesis]. NSYSU; 2006. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0801106-224333

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

49. PEIREIRA, Alysson Bispo. Sistemas de recomendação baseados em contexto físico e social .

Degree: 2016, Universidade Federal de Pernambuco

 Em meio a grande sobrecarga de dados disponíveis na internet, sistemas de recomendação tornam-se ferramentas indispensáveis para auxiliar usuários no encontro de itens ou conteúdos… (more)

Subjects/Keywords: Sistemas de Recomendação; Contexto Social; Contexto Físico; Filtragem Colaborativa; Recommender Systems; Social Context; Physical Context; Collaborative Filtering; Post Filtering

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

APA (6th Edition):

PEIREIRA, A. B. (2016). Sistemas de recomendação baseados em contexto físico e social . (Thesis). Universidade Federal de Pernambuco. Retrieved from https://repositorio.ufpe.br/handle/123456789/19521

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

PEIREIRA, Alysson Bispo. “Sistemas de recomendação baseados em contexto físico e social .” 2016. Thesis, Universidade Federal de Pernambuco. Accessed June 16, 2019. https://repositorio.ufpe.br/handle/123456789/19521.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

PEIREIRA, Alysson Bispo. “Sistemas de recomendação baseados em contexto físico e social .” 2016. Web. 16 Jun 2019.

Vancouver:

PEIREIRA AB. Sistemas de recomendação baseados em contexto físico e social . [Internet] [Thesis]. Universidade Federal de Pernambuco; 2016. [cited 2019 Jun 16]. Available from: https://repositorio.ufpe.br/handle/123456789/19521.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

PEIREIRA AB. Sistemas de recomendação baseados em contexto físico e social . [Thesis]. Universidade Federal de Pernambuco; 2016. Available from: https://repositorio.ufpe.br/handle/123456789/19521

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Georgia Tech

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

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 June 16, 2019. http://hdl.handle.net/1853/53846.

MLA Handbook (7th Edition):

Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Web. 16 Jun 2019.

Vancouver:

Lee J. Local approaches for collaborative filtering. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Jun 16]. 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


NSYSU

51. Tseng, Ching-Ju. Cluster-based Collaborative Filtering Recommendation Approach.

Degree: Master, Information Management, 2003, NSYSU

 Recommendation is not a new phenomenon arising from the digital era, but an existing social behavior in real life. Recommendation systems facilitate such natural social… (more)

Subjects/Keywords: Recommendation Systems; Clustering; Cluster-based Collaborative Filtering Recommendation; Preference Prediction; Collaborative Filtering Recommendation

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

APA (6th Edition):

Tseng, C. (2003). Cluster-based Collaborative Filtering Recommendation Approach. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0812103-164119

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Tseng, Ching-Ju. “Cluster-based Collaborative Filtering Recommendation Approach.” 2003. Thesis, NSYSU. Accessed June 16, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0812103-164119.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Tseng, Ching-Ju. “Cluster-based Collaborative Filtering Recommendation Approach.” 2003. Web. 16 Jun 2019.

Vancouver:

Tseng C. Cluster-based Collaborative Filtering Recommendation Approach. [Internet] [Thesis]. NSYSU; 2003. [cited 2019 Jun 16]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0812103-164119.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tseng C. Cluster-based Collaborative Filtering Recommendation Approach. [Thesis]. NSYSU; 2003. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0812103-164119

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Northeastern University

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

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 June 16, 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. 16 Jun 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 Jun 16]. 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


University of Colorado

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

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 June 16, 2019. http://scholar.colorado.edu/csci_gradetds/3.

MLA Handbook (7th Edition):

Gartrell, Charles Michael. “Enhancing Recommender Systems Using Social Indicators.” 2014. Web. 16 Jun 2019.

Vancouver:

Gartrell CM. Enhancing Recommender Systems Using Social Indicators. [Internet] [Doctoral dissertation]. University of Colorado; 2014. [cited 2019 Jun 16]. 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


Australian National University

54. Sedhain, Suvash. A Unified Approach to Collaborative Filtering via Linear Models and Beyond .

Degree: 2016, Australian National University

 Recommending a personalised list of items to users is a core task for many online services such as Amazon, Netflix, and Youtube. Recommender systems are… (more)

Subjects/Keywords: Recommender System; One-Class Collaborative Filtering; Cold-Start Recommendation; Social Recommendation; Rating Prediction; Deep Learning

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

APA (6th Edition):

Sedhain, S. (2016). A Unified Approach to Collaborative Filtering via Linear Models and Beyond . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/118270

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sedhain, Suvash. “A Unified Approach to Collaborative Filtering via Linear Models and Beyond .” 2016. Thesis, Australian National University. Accessed June 16, 2019. http://hdl.handle.net/1885/118270.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sedhain, Suvash. “A Unified Approach to Collaborative Filtering via Linear Models and Beyond .” 2016. Web. 16 Jun 2019.

Vancouver:

Sedhain S. A Unified Approach to Collaborative Filtering via Linear Models and Beyond . [Internet] [Thesis]. Australian National University; 2016. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/1885/118270.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sedhain S. A Unified Approach to Collaborative Filtering via Linear Models and Beyond . [Thesis]. Australian National University; 2016. Available from: http://hdl.handle.net/1885/118270

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


KTH

55. Johansson, Jonas. A sentiment analysis approach to manage the new item problem of Slope One.

Degree: Computer Science and Communication (CSC), 2017, KTH

This report targets a specific problem for recommender algorithms which is the new item problem and propose a method with sentiment analysis as the… (more)

Subjects/Keywords: sentiment analysis; slope one; collaborative filtering; new item problem; cold start; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Johansson, J. (2017). A sentiment analysis approach to manage the new item problem of Slope One. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208667

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Johansson, Jonas. “A sentiment analysis approach to manage the new item problem of Slope One.” 2017. Thesis, KTH. Accessed June 16, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208667.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Johansson, Jonas. “A sentiment analysis approach to manage the new item problem of Slope One.” 2017. Web. 16 Jun 2019.

Vancouver:

Johansson J. A sentiment analysis approach to manage the new item problem of Slope One. [Internet] [Thesis]. KTH; 2017. [cited 2019 Jun 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208667.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Johansson J. A sentiment analysis approach to manage the new item problem of Slope One. [Thesis]. KTH; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208667

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Youngstown State University

56. Curnalia, James W. The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen.

Degree: Master of Computing and Information Systems, Department of Computer Science and Information Systems, 2013, Youngstown State University

 More and more outlets are utilizing collaborative filtering techniques to make sense of the sea of data generated by our hyper-connected world. How a collaborative(more)

Subjects/Keywords: Artificial Intelligence; Computer Science; Statistics; collaborative filtering; recommendation; model; training; machine learning; algorithms

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

APA (6th Edition):

Curnalia, J. W. (2013). The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen. (Masters Thesis). Youngstown State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370016838

Chicago Manual of Style (16th Edition):

Curnalia, James W. “The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen.” 2013. Masters Thesis, Youngstown State University. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370016838.

MLA Handbook (7th Edition):

Curnalia, James W. “The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen.” 2013. Web. 16 Jun 2019.

Vancouver:

Curnalia JW. The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen. [Internet] [Masters thesis]. Youngstown State University; 2013. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370016838.

Council of Science Editors:

Curnalia JW. The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen. [Masters Thesis]. Youngstown State University; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370016838


Queensland University of Technology

57. Tang, Xiaoyu. Multidimensional recommendation framework based on the incorporation of nearest neighbourhood and tensor factorisation.

Degree: 2016, Queensland University of Technology

 This thesis investigated in depth the distinctive relations between users, items, and tags in recommender systems. This thesis proposed a number of novel techniques to… (more)

Subjects/Keywords: Collaborative Filtering; User Profiling; Recommender Systems; Multidimensional datasets; Folksonomy; Tags; Taxonomy; Personalisation

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

APA (6th Edition):

Tang, X. (2016). Multidimensional recommendation framework based on the incorporation of nearest neighbourhood and tensor factorisation. (Thesis). Queensland University of Technology. Retrieved from http://eprints.qut.edu.au/96195/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Tang, Xiaoyu. “Multidimensional recommendation framework based on the incorporation of nearest neighbourhood and tensor factorisation.” 2016. Thesis, Queensland University of Technology. Accessed June 16, 2019. http://eprints.qut.edu.au/96195/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Tang, Xiaoyu. “Multidimensional recommendation framework based on the incorporation of nearest neighbourhood and tensor factorisation.” 2016. Web. 16 Jun 2019.

Vancouver:

Tang X. Multidimensional recommendation framework based on the incorporation of nearest neighbourhood and tensor factorisation. [Internet] [Thesis]. Queensland University of Technology; 2016. [cited 2019 Jun 16]. Available from: http://eprints.qut.edu.au/96195/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tang X. Multidimensional recommendation framework based on the incorporation of nearest neighbourhood and tensor factorisation. [Thesis]. Queensland University of Technology; 2016. Available from: http://eprints.qut.edu.au/96195/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


NSYSU

58. Ke, Sheng-Jhe. A Classification Model with Data Analysis for Improving Collaborative Filtering: An Experimental Study on Epinions.com.

Degree: Master, Information Management, 2015, NSYSU

Collaborative Filtering is one of the most popular methods to implement recommender systems. It can predict usersâ ratings to generate personalized product recommendations based on… (more)

Subjects/Keywords: recommender system; user similarity; collaborative filtering; trust metric; decision tree; classification; trust propagation

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

APA (6th Edition):

Ke, S. (2015). A Classification Model with Data Analysis for Improving Collaborative Filtering: An Experimental Study on Epinions.com. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726115-133439

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ke, Sheng-Jhe. “A Classification Model with Data Analysis for Improving Collaborative Filtering: An Experimental Study on Epinions.com.” 2015. Thesis, NSYSU. Accessed June 16, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726115-133439.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ke, Sheng-Jhe. “A Classification Model with Data Analysis for Improving Collaborative Filtering: An Experimental Study on Epinions.com.” 2015. Web. 16 Jun 2019.

Vancouver:

Ke S. A Classification Model with Data Analysis for Improving Collaborative Filtering: An Experimental Study on Epinions.com. [Internet] [Thesis]. NSYSU; 2015. [cited 2019 Jun 16]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726115-133439.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ke S. A Classification Model with Data Analysis for Improving Collaborative Filtering: An Experimental Study on Epinions.com. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726115-133439

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Uppsala University

59. Strömqvist, Zakris. Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?.

Degree: Statistics, 2018, Uppsala University

  One of the most popular methods in recommender systems are matrix factorization (MF) models. In this paper, the sensitivity of sparsity of these models… (more)

Subjects/Keywords: Recommender systems; Collaborative filtering; Matrix factorization; Probability Theory and Statistics; Sannolikhetsteori och statistik

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

APA (6th Edition):

Strömqvist, Z. (2018). Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352653

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Strömqvist, Zakris. “Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?.” 2018. Thesis, Uppsala University. Accessed June 16, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352653.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Strömqvist, Zakris. “Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?.” 2018. Web. 16 Jun 2019.

Vancouver:

Strömqvist Z. Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?. [Internet] [Thesis]. Uppsala University; 2018. [cited 2019 Jun 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352653.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Strömqvist Z. Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?. [Thesis]. Uppsala University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352653

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Penn State University

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

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 June 16, 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. 16 Jun 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 Jun 16]. 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

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