A Recommender System using Principal Component Analysis

In this paper we examine the use of a mathematical procedure, called Principal Component Analysis, in Recommender Systems. The resulting filtering algorithm applies PCA on user ratings and demographic data, aiming to improve various aspects of the recommendation process. After a brief introduction to PCA, we provide a discussion of the proposed PCADemog algorithm, along with possible ways of combining it with different sources of filtering data. The experimental part of this work tests distinct parameterizations for PCA-Demog, identifying those with the best performance. Finally, the paper compares their results with those achieved by other filtering approaches, and draws interesting conclusions.

[1]  John Riedl,et al.  An Algorithmic Framework for Performing Collaborative Filtering , 1999, SIGIR Forum.

[2]  Michael J. Pazzani,et al.  Learning Collaborative Information Filters , 1998, ICML.

[3]  Hans-Peter Kriegel,et al.  Statistical Learning Approaches to Information Filtering , 2004 .

[4]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[5]  Konstantinos G. Margaritis,et al.  Recommender Systems : An Experimental Comparison of two Filtering Algorithms , 2003 .

[6]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[7]  Konstantinos G. Margaritis,et al.  On the enhancement of collaborative filtering by demographic data , 2006, Web Intell. Agent Syst..

[8]  Prem Melville and Raymond J. Mooney and Ramadass Nagarajan.  Content-Boosted Collaborative Filtering , 2001 .

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

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

[11]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[12]  Chuck P. Lam.  Collaborative Filtering Using Associative Neural Memory , 2003, ITWP.

[13]  Lawrence K. Saul,et al.  A Generalized Linear Model for Principal Component Analysis of Binary Data , 2003, AISTATS.

[14]  John Riedl,et al.  Sparsity, scalability, and distribution in recommender systems , 2001 .

[15]  Konstantinos G. Margaritis,et al.  Applying SVD on Generalized Item-based Filtering , 2006, Int. J. Comput. Sci. Appl..

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

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

[18]  Mark W. Newman,et al.  SWAMI: a framework for collaborative filtering algorithm development and evaluation. , 2000, SIGIR 2000.

[19]  Peter J. Bentley,et al.  Particle swarm optimization recommender system , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).