Cubic Analysis of Social Bookmarking for Personalized Recommendation

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...

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Published inFrontiers of WWW Research and Development - APWeb 2006 pp. 733 - 738
Main Authors Xu, Yanfei, Zhang, Liang, Liu, Wei
Format Book Chapter
LanguageEnglish
Japanese
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
SeriesLecture Notes in Computer Science
Subjects
Collaborative Filter
Division Algorithm
Latent Semantic Indexing
Personalized Recommendation
Social Bookmark
Online AccessGet full text
ISBN3540311424
9783540311423
ISSN0302-9743
1611-3349
DOI10.1007/11610113_66

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Abstract 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.
AbstractList 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.
Author Liu, Wei
Zhang, Liang
Xu, Yanfei
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Editor Li, Jianzhong
Kitsuregawa, Masaru
Zhou, Xiaofang
Zhang, Yanchun
Shen, Heng Tao
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Snippet Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful...
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SubjectTerms Collaborative Filter
Division Algorithm
Latent Semantic Indexing
Personalized Recommendation
Social Bookmark
Title Cubic Analysis of Social Bookmarking for Personalized Recommendation
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