Leveraging aggregate ratings for improving predictive performance of recommender systems

One of the key problems in recommender systems is accurate estimation of unknown ratings of individual items for individual users in terms of the previously specified ratings and other characteristics of items and users. In this thesis, we investigate a way of improving estimations of individual ratings using externally provided properties of aggregate ratings for groups of items and users, such as an externally specified average rating of action movies provided by graduate students or externally specified standard deviation of ratings for comedy movies.

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

[2]  G. A. Marcoulides.  Multilevel Analysis Techniques and Applications , 2002 .

[3]  W. Greene,et al.  计量经济分析 = Econometric analysis , 2009 .

[4]  Akhmed Umyarov,et al.  Improving Collaborative Filtering Recommendations Using External Data , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[5]  G. Molenberghs,et al.  Linear Mixed Models for Longitudinal Data , 2001 .

[6]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[7]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[8]  Gediminas Adomavicius,et al.  Multidimensional Recommender Systems: A Data Warehousing Approach , 2001, WELCOM.

[9]  R. Bhatia.  Positive Definite Matrices , 2007 .

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

[11]  Robert A. Lordo,et al.  Nonparametric and Semiparametric Models , 2005, Technometrics.

[12]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[13]  R. Kohli,et al.  Internet Recommendation Systems , 2000 .

[14]  Andrei Z. Broder,et al.  Estimating rates of rare events at multiple resolutions , 2007, KDD '07.

[15]  Akhmed Umyarov,et al.  Leveraging aggregate ratings for better recommendations , 2007, RecSys '07.

[16]  W. Härdle.  Nonparametric and Semiparametric Models , 2004 .

[17]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[18]  Barry Smyth,et al.  Group recommender systems: a critiquing based approach , 2006, IUI '06.

[19]  Anthony S. Bryk,et al.  Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .

[20]  A. Neumaier,et al.  Restricted maximum likelihood estimation of covariances in sparse linear models , 1998, Genetics Selection Evolution.

[21]  Yehuda Koren,et al.  Modeling relationships at multiple scales to improve accuracy of large recommender systems , 2007, KDD '07.

[22]  James Bennett,et al.  The Netflix Prize , 2007 .

[23]  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.

[24]  Alexander Tuzhilin,et al.  The long tail of recommender systems and how to leverage it , 2008, RecSys '08.

[25]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[26]  Barry Smyth,et al.  Recommendation to Groups , 2007, The Adaptive Web.

[27]  Gediminas Adomavicius,et al.  Incorporating contextual information in recommender systems using a multidimensional approach , 2005, TOIS.

[28]  Ioannis Karatzas,et al.  Brownian Motion and Stochastic Calculus , 1987 .

[29]  Christian Posse,et al.  Bayesian Mixed-Effects Models for Recommender Systems , 1999 .

[30]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[31]  John Riedl,et al.  PolyLens: A recommender system for groups of user , 2001, ECSCW.

[32]  Anton Schwaighofer,et al.  Learning Gaussian Process Kernels via Hierarchical Bayes , 2004, NIPS.

[33]  Johan Bollen.  Group User Models for Personalized Hyperlink Recommendations , 2000, AH.

[34]  Steven M. Lalonde,et al.  A First Course in Multivariate Statistics , 1997, Technometrics.