Cubic Analysis of Social Bookmarking for Personalized Recommendation

• Published in: Frontiers of WWW Research and Development - APWeb 2006 pp. 733 - 738
• Main Authors: Xu, Yanfei, Zhang, Liang, Liu, Wei
• Format: Book Chapter
• Language: English; Japanese
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006
• Series: Lecture Notes in Computer Science
• Subjects: Collaborative Filter; Division Algorithm; Latent Semantic Indexing; Personalized Recommendation; Social Bookmark
• ISBN: 3540311424; 9783540311423
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11610113_66

Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods.