Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce

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[8]  Wang Wei.  Hybrid Partial Entropy and Relative Entropy , 2005 .

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[11]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[12]  David M. Pennock,et al.  A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains , 2002, NIPS.

[13]  Zeng Yan.  Collaborative Filtering Recommendation Based on Content and Item Rating Prediction , 2004 .

[14]  Wang Xia.  Research on application of item-based collaborative filtering in recommender systems , 2007 .

[15]  Hu Nai,et al.  ALGORITHM DESIGN FOR PERSONALIZATION RECOMMENDATION SYSTEMS , 2002 .

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

[17]  Arnd Kohrs,et al.  Improving collaborative filtering for new-users by smart object selection , 2001 .

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

[20]  Charu C. Aggarwal,et al.  On the effects of dimensionality reduction on high dimensional similarity search , 2001, PODS.

[21]  Xin Jin,et al.  Task-Oriented Web User Modeling for Recommendation , 2005, User Modeling.

[22]  Takeo Kanade,et al.  Maximum Entropy for Collaborative Filtering , 2004, UAI.

[23]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[24]  Jennifer Widom,et al.  Exploiting hierarchical domain structure to compute similarity , 2003, TOIS.

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

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