Ieee Transactions on Knowledge and Data Engineering Probabilistic Memory-based Collaborative Filtering

Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper, we develop a probabilistic framework for memory-based CF (PMCF). While this framework has clear links with classical memory-based CF, it allows us to find principled solutions to known problems of CF-based recommender systems. In particular, we show that a probabilistic active learning method can be used to actively query the user, thereby solving the "new user problem." Furthermore, the probabilistic framework allows us to reduce the computational cost of memory-based CF by working on a carefully selected subset of user profiles, while retaining high accuracy. We report experimental results based on two real-world data sets, which demonstrate that our proposed PMCF framework allows an accurate and efficient prediction of user preferences.

[1]  David H. Bailey,et al.  Algorithms and applications , 1988 .

[2]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[3]  David Heckerman,et al.  Troubleshooting Under Uncertainty , 1994 .

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

[5]  David D. Lewis,et al.  Heterogeneous Uncertainty Sampling for Supervised Learning , 1994, ICML.

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

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

[8]  Michael J. Pazzani,et al.  Syskill & Webert: Identifying Interesting Web Sites , 1996, AAAI/IAAI, Vol. 1.

[9]  Ronald L. Wasserstein,et al.  Monte Carlo: Concepts, Algorithms, and Applications , 1997 .

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

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

[12]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[13]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

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

[15]  John Riedl,et al.  Combining Collaborative Filtering with Personal Agents for Better Recommendations , 1999, AAAI/IAAI.

[16]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

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

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

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

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

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

[22]  Eric Horvitz,et al.  Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach , 2000, UAI.

[23]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[24]  Sergio A. Alvarez,et al.  Collaborative Recommendation via Adaptive Association Rule Mining , 2000 .

[25]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[26]  Daphne Koller,et al.  Active learning: theory and applications , 2001 .

[27]  Wee Sun Lee.  Collaborative Learning for Recommender Systems , 2001 .

[28]  Wee Sun Lee.  Collaborative Learning and Recommender Systems , 2001, ICML.

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

[30]  Tong Zhang,et al.  Recommender Systems Using Linear Classifiers , 2002 .

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

[32]  Wei-Ying Ma,et al.  Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes , 2002, UAI.

[33]  Sergio A. Alvarez,et al.  Efficient Adaptive-Support Association Rule Mining for Recommender Systems , 2004, Data Mining and Knowledge Discovery.

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