Cubic Analysis of Social Bookmarking for Personalized Recommendation
Y. Xu, L. Zhang, and W. Liu. Frontiers of WWW Research and Development - APWeb 2006, (2006)
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 -
%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 bookmarking collaborative filtering recommendersystems social tagging tagging_proposal
%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 -
@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 = {https://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 = {2008-08-13T11:01:55.000+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
}