A collaborative recommender system based on probabilistic inference from fuzzy observations

The problem of building recommender systems has attracted considerable attention in recent years. The objective of this paper is to automatically suggest and rank a list of new items to a user based on the past voting patterns of other users with similar tastes. The proposed model can be considered as a Soft Computing-based collaborative recommender system. The combination of Bayesian networks, which enables an intuitive representation of the mechanisms that govern the relationships between the users, and the Fuzzy Set Theory, enabling us to represent ambiguity or vagueness in the description of the ratings, improves the accuracy of the system.

[1]  Chung-Hsing Yeh,et al.  Fuzzy multilingual information filtering , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

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

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

[4]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[5]  Eyke Hüllermeier,et al.  Fuzzy methods for case-based recommendation and decision support , 2006, Journal of Intelligent Information Systems.

[6]  Analía Amandi,et al.  User profiling with Case-Based Reasoning and Bayesian Networks , 2000, IBERAMIA-SBIA 2000 Open Discussion Track.

[7]  Michael J. Pazzani,et al.  Collaborative Filtering with the Simple Bayesian Classifier , 2000, PRICAI.

[8]  Herbert Toth,et al.  Probabilities and fuzzy events: an operational approach , 1992 .

[9]  Cory J. Butz.  Exploiting contextual independencies in Web search and user profiling , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

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

[11]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[12]  Bradley N. Miller,et al.  MovieLens unplugged: experiences with an occasionally connected recommender system , 2003, IUI '03.

[13]  Rami Zwick,et al.  Measures of similarity among fuzzy concepts: A comparative analysis , 1987, Int. J. Approx. Reason..

[14]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[15]  Byeong Man Kim,et al.  Clustering approach for hybrid recommender system , 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

[16]  Luis M. de Campos,et al.  A Decision-Based Approach for Recommending in Hierarchical Domains , 2005, ECSQARU.

[17]  Ning Zhong,et al.  Bayesian Networks Structure Learning and Its Application to Personalized Recommendation in a B2C Portal , 2004, IEEE/WIC/ACM International Conference on Web Intelligence (WI'04).

[18]  S. Moral,et al.  On the concept of possibility-probability consistency , 1987 .

[19]  W. Pedrycz,et al.  Construction of fuzzy models through clustering techniques , 1993 .

[20]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[21]  José M. Molina López,et al.  Agent-based collaborative filtering based on fuzzy recommendations , 2004, Int. J. Web Eng. Technol..

[22]  L. Zadeh.  Probability measures of Fuzzy events , 1968 .

[23]  D. Dubois,et al.  On Possibility/Probability Transformations , 1993 .

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

[25]  Lotfi A. Zadeh,et al.  Fuzzy sets and systems , 1990 .

[26]  Xiaofeng Liao,et al.  Applying Fuzzy Logic to Recommend Consumer Electronics , 2005, ICDCIT.

[27]  David M. Pennock,et al.  Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments , 2001, UAI.

[28]  Sonja Kangas.  Collaborative Filtering and Recommendation Systems Version 1 . 0 22 . 1 . 2002 , 2002 .

[29]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[30]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[31]  Xin Jin,et al.  Semantically Enhanced Collaborative Filtering on the Web , 2003, EWMF.

[32]  Yi Zhang,et al.  Bayesian adaptive user profiling with explicit & implicit feedback , 2006, CIKM '06.

[33]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[34]  Bong-Jin Yum,et al.  Collaborative filtering based on iterative principal component analysis , 2005, Expert Syst. Appl..

[35]  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.

[36]  Thomas Hofmann,et al.  Latent Class Models for Collaborative Filtering , 1999, IJCAI.

[37]  Ronald R. Yager,et al.  Probabilities from fuzzy observations , 1984, Inf. Sci..

[38]  Antonio González,et al.  A learning methodology in uncertain and imprecise environments , 1995 .

[39]  Jian Chen,et al.  Recommendation Based on Influence Sets , 2006 .

[40]  Nicolas Werro,et al.  Recommending Products with a Fuzzy Classification. , 2006 .

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

[42]  Shyi-Ming Chen,et al.  A comparison of similarity measures of fuzzy values , 1995 .

[43]  G. Klir,et al.  PROBABILITY-POSSIBILITY TRANSFORMATIONS: A COMPARISON , 1992 .

[44]  María S. Pérez-Hernández,et al.  Collaborative Filtering Using Interval Estimation Naïve Bayes , 2003, AWIC.

[45]  D. Heckerman,et al.  Dependency networks for inference , 2000 .

[46]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[47]  Ronald R. Yager,et al.  Fuzzy logic methods in recommender systems , 2003, Fuzzy Sets Syst..

[48]  Alan F. Smeaton,et al.  Improving the Quality of the Personalized Electronic Program Guide , 2004, User Modeling and User-Adapted Interaction.

[49]  David Maxwell Chickering,et al.  Dependency Networks for Inference, Collaborative Filtering, and Data Visualization , 2000, J. Mach. Learn. Res..

[50]  JinHongKim,et al.  XFC - XML based on fuzzy clustering - method for personalized user profile based on recommendation system , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..