Big Data Analytics and Recommender Systems

Recommender systems are designed to augment human decision making. The objective of a recommender system is to suggest relevant items for a user to choose from a plethora of options. In essence, recommender systems are concerned about predicting personalized item choices for a user. Recommender systems produce a ranked list of items ordered in their order of likeability for the user.

[1]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[2]  Ricardo A. Baeza-Yates,et al.  Query Recommendation Using Query Logs in Search Engines , 2004, EDBT Workshops.

[3]  Tommi S. Jaakkola,et al.  Weighted Low-Rank Approximations , 2003, ICML.

[4]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[5]  R. Dieng-Kuntz,et al.  A Graph-Based Algorithm for Alignment of OWL Ontologies , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[6]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[7]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

[8]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[9]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[10]  Christian Schmitt,et al.  Multivariate Preference Models and Decision Making with the MAUT Machine , 2003, User Modeling.

[11]  Stuart E. Middleton,et al.  Ontological user profiling in recommender systems , 2004, TOIS.

[12]  Saul Vargas,et al.  Rank and relevance in novelty and diversity metrics for recommender systems , 2011, RecSys '11.

[13]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[14]  Bamshad Mobasher,et al.  Segment-based injection attacks against collaborative filtering recommender systems , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[15]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[16]  Shuang-Hong Yang,et al.  Collaborative competitive filtering: learning recommender using context of user choice , 2011, SIGIR.

[17]  Barry Smyth,et al.  PTV: Intelligent Personalised TV Guides , 2000, AAAI/IAAI.

[18]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[19]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[20]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[21]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[22]  John K. Debenham,et al.  Informed Recommender: Basing Recommendations on Consumer Product Reviews , 2007, IEEE Intelligent Systems.

[23]  Ken Lang,et al.  NewsWeeder: Learning to Filter Netnews , 1995, ICML.

[24]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[25]  Osmar R. Zaïane,et al.  Combining Usage, Content, and Structure Data to Improve Web Site Recommendation , 2004, EC-Web.

[26]  Sean M. McNee,et al.  Getting to know you: learning new user preferences in recommender systems , 2002, IUI '02.

[27]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[28]  Jaideep Srivastava,et al.  WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles , 2003, Lecture Notes in Computer Science.

[29]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[30]  Nathan Srebro,et al.  Fast maximum margin matrix factorization for collaborative prediction , 2005, ICML.

[31]  John Riedl,et al.  Shilling recommender systems for fun and profit , 2004, WWW '04.

[32]  Lora Aroyo,et al.  User model elicitation and enrichment for context-sensitive personalization in a multiplatform tv environment , 2009, EuroITV '09.

[33]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[34]  Xiaoyuan Su,et al.  Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[35]  Analía Amandi,et al.  Intelligent User Profiling , 2009, Artificial Intelligence: An International Perspective.