BibSonomy :: BibTeX ::

tag user group author concept BibTeX key search:all search:jaeschke

A blue social bookmark and publication sharing system.

entry of jaeschke and 6 other people: spam

Cubic Analysis of Social Bookmarking for Personalized Recommendation

by: Yanfei Xu and Liang Zhang and Wei Liu

In: Frontiers of WWW Research and Development - APWeb 2006 , Vol. 3841Springer (2006) , p. 733--738.

Resources (URL, PDF, PS...)

URL:	http://dx.doi.org/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 usersâ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods. ER -

BibTeX record

@inproceedings{yanfei2006cubic,
  author = {Yanfei Xu and Liang Zhang and Wei Liu},
  booktitle = {APWeb},
  editor = {Xiaofang Zhou and Jianzhong Li and Heng Tao Shen and Masaru Kitsuregawa and Yanchun Zhang},
  interHash = {edf999afa5a0ff81e53b0c859b466659},
  intraHash = {98dd99b5f4189c8427163fd5a7568e1d},
  journal = {Frontiers of WWW Research and Development - APWeb 2006},
  pages = {733--738},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation},
  url = {http://dx.doi.org/10.1007/11610113_66},
  volume = {3841},
  year = {2006},
  ee = {http://dx.doi.org/10.1007/11610113_66},
  isbn = {3-540-31142-4},
  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 usersâ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods.
ER  -}
}

Endnote record

%0 Conference Paper
%1 yanfei2006cubic
%A Yanfei Xu
%A Liang Zhang
%A Wei Liu
%B APWeb
%@ 3-540-31142-4
%D 2006
%E Xiaofang Zhou
%E Jianzhong Li
%E Heng Tao Shen
%E Masaru Kitsuregawa
%E Yanchun Zhang
%I Springer
%J Frontiers of WWW Research and Development - APWeb 2006
%K 
%P 733--738
%T Cubic Analysis of Social Bookmarking for Personalized Recommendation
%U http://dx.doi.org/10.1007/11610113_66
%V 3841
%X 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 usersâ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods.
ER  -

BibSonomy :: BibTeX ::

Cubic Analysis of Social Bookmarking for Personalized Recommendation

Resources (URL, PDF, PS...)

Abstract

BibTeX record

Endnote record