A statistical model for user preference

Modeling user preference is one of the challenging issues in intelligent information systems. Extensive research has been performed to automatically analyze user preference and to utilize it. One problem still remains: The representation of preference, usually given by measure of vector similarity or probability, does not always correspond to common sense of preference. This problem gets worse in the case of negative preference. To overcome this problem, this paper presents a preference model using mutual information in a statistical framework. This paper also presents a method that combines information of joint features and alleviates problems arising from sparse data. Experimental results, compared with the previous recommendation models, show that the proposed model has the highest accuracy in recommendation tests.

[1]  Ricardo Baeza-Yates,et al.  Information Retrieval: Data Structures and Algorithms , 1992 .

[2]  Wentian Li,et al.  Random texts exhibit Zipf's-law-like word frequency distribution , 1992, IEEE Trans. Inf. Theory.

[3]  Venkata Subramaniam,et al.  Information Retrieval: Data Structures & Algorithms , 1992 .

[4]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

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

[6]  Benjamin W. Wah,et al.  Editorial: Two Named to Editorial Board of IEEE Transactions on Knowledge and Data Engineering , 1996 .

[7]  Sung-Young Jung,et al.  Markov random field based English Part-Of-Speech tagging system , 1996, COLING.

[8]  Thorsten Joachims,et al.  A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization , 1997, ICML.

[9]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

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

[11]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[12]  Naoki Abe,et al.  Collaborative Filtering Using Weighted Majority Prediction Algorithms , 1998, ICML.

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

[14]  Michael J. Pazzani,et al.  A hybrid user model for news story classification , 1999 .

[15]  Lada A. Adamic.  Zipf, Power-laws, and Pareto-a ranking tutorial , 2000 .

[16]  Gediminas Adomavicius,et al.  Using Data Mining Methods to Build Customer Profiles , 2001, Computer.

[17]  W. Reed.  The Pareto, Zipf and other power laws , 2001 .

[18]  Manfred K. Warmuth,et al.  Learning Binary Relations Using Weighted Majority Voting , 1995, Machine Learning.

[19]  Rajeev Motwani,et al.  Beyond Market Baskets: Generalizing Association Rules to Dependence Rules , 1998, Data Mining and Knowledge Discovery.

[20]  Tong Zhang,et al.  Text Categorization Based on Regularized Linear Classification Methods , 2001, Information Retrieval.