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Cubic Analysis of Social Bookmarking for Personalized Recommendation

by: Yanfei Xu, Liang Zhang, and Wei Liu

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

Resources (URL, PDF, PS...)

URL:	http://dx.doi.org/10.1007/11610113_66 http://books.google.com/books?vid=ISBN3540311424&id=DyIAdHBP28gC&pg=PA733&lpg=PA733&dq=%22social%20bookmarking%22&hl=de&sig=4lpkt4G4qyZOiWgDQoH_Cpq6EJw

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.ER -

BibTeX record

@article{XuZL06,
  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.ER -},
  added-at = {2008-08-13T11:00:11.000+0200},
  author = {Xu, Yanfei and Zhang, Liang and Liu, Wei},
  biburl = {http://www.bibsonomy.org/bibtex/25fbd24f07fe8784b516e69b0eb3192f3/michael},
  interhash = {edf999afa5a0ff81e53b0c859b466659},
  intrahash = {5fbd24f07fe8784b516e69b0eb3192f3},
  journal = {Frontiers of WWW Research and Development - APWeb 2006},
  keywords = {bookmarking collaborative filtering recommendersystems social tagging tagging_proposal},
  owner = {michael},
  pages = {733--738},
  timestamp = {2008-08-13T11:00:11.000+0200},
  title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation},
  url = {http://dx.doi.org/10.1007/11610113_66 http://books.google.com/books?vid=ISBN3540311424&id=DyIAdHBP28gC&pg=PA733&lpg=PA733&dq=%22social%20bookmarking%22&hl=de&sig=4lpkt4G4qyZOiWgDQoH_Cpq6EJw},
  year = 2006
}

Endnote record

%0 Journal Article
%1 XuZL06
%A Xu, Yanfei
%A Zhang, Liang
%A Liu, Wei
%D 2006
%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 http://books.google.com/books?vid=ISBN3540311424&id=DyIAdHBP28gC&pg=PA733&lpg=PA733&dq=%22social%20bookmarking%22&hl=de&sig=4lpkt4G4qyZOiWgDQoH_Cpq6EJw
%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 users�?? various interests, and make better personalized
	recommendation based on them. Experiment reveals the superiority
	of our method over traditional CF methods.ER -

BibSonomy :: publication ::

Cubic Analysis of Social Bookmarking for Personalized Recommendation

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Abstract

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Endnote record