BibSonomyburst/bibtex/1edf999afa5a0ff81e53b0c859b466659BibSonomy RSS feed for /bibtex/1edf999afa5a0ff81e53b0c859b4666592012-02-17T15:53:43+01:00http://www.bibsonomy.org/bibtex/2c7e9508597eb4942858540dfc9c6cea9/zenozeno2009-12-17T17:15:39+01:002006 bookmarks recsys <span class="authorEditorList"><a href="/author/Xu">Yanfei Xu</a>, <a href="/author/Zhang">Liang Zhang</a>, and <a href="/author/Liu">Wei Liu</a> </span><em>page 733--738. </em>(<em>2006</em>)Thu Dec 17 17:15:39 CET 2009Frontiers of WWW Research and Development - APWeb 2006733--738Cubic Analysis of Social Bookmarking for Personalized Recommendation20062006 bookmarks recsys http://www.bibsonomy.org/bibtex/264d7db1df158a73984b5483873c42cfd/ocelmaocelma2009-01-08T17:06:55+01:00PhD bookmarking folksonomy kdubiq recommender social summerschool tagging toread <span class="authorEditorList"><a href="/author/Xu">Yanfei Xu</a>, <a href="/author/Zhang">Liang Zhang</a>, and <a href="/author/Liu">Wei Liu</a> </span><em>Frontiers of WWW Research and Development</em> (<em>2006</em>)Thu Jan 08 17:06:55 CET 2009Frontiers of WWW Research and Development733--738Cubic Analysis of Social Bookmarking for Personalized Recommendation2006PhD bookmarking folksonomy kdubiq recommender social summerschool tagging toread 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 -SpringerLinkhttp://www.bibsonomy.org/bibtex/298dd99b5f4189c8427163fd5a7568e1d/jaeschkejaeschke2008-11-05T11:19:04+01:00folksonomy recommender tag <span class="authorEditorList"><a href="/author/Xu">Yanfei Xu</a>, <a href="/author/Zhang">Liang Zhang</a>, and <a href="/author/Liu">Wei Liu</a> </span><em>APWeb, </em><em>volume 3841 of Lecture Notes in Computer Science, </em><em>page 733--738. </em><em>Springer, </em>(<em>2006</em>)Wed Nov 05 11:19:04 CET 2008APWebFrontiers of WWW Research and Development - APWeb 2006733--738Lecture Notes in Computer ScienceCubic Analysis of Social Bookmarking for Personalized Recommendation38412006folksonomy recommender tag 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 -http://www.bibsonomy.org/bibtex/25fbd24f07fe8784b516e69b0eb3192f3/michaelmichael2008-08-13T11:00:11+02:00bookmarking collaborative filtering recommendersystems social tagging tagging_proposal <span class="authorEditorList"><a href="/author/Xu">Yanfei Xu</a>, <a href="/author/Zhang">Liang Zhang</a>, and <a href="/author/Liu">Wei Liu</a> </span><em>Frontiers of WWW Research and Development - APWeb 2006</em> (<em>2006</em>)Wed Aug 13 11:00:11 CEST 2008Frontiers of WWW Research and Development - APWeb 2006733--738Cubic Analysis of Social Bookmarking for Personalized Recommendation2006bookmarking collaborative filtering recommendersystems social tagging tagging_proposal 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 -http://www.bibsonomy.org/bibtex/25fbd24f07fe8784b516e69b0eb3192f3/wnpxrzwnpxrz2007-11-19T15:48:13+01:00bookmarking collaborative filtering recommendersystems social tagging <span class="authorEditorList"><a href="/author/Xu">Yanfei Xu</a>, <a href="/author/Zhang">Liang Zhang</a>, and <a href="/author/Liu">Wei Liu</a> </span><em>Frontiers of WWW Research and Development - APWeb 2006</em> (<em>2006</em>)Mon Nov 19 15:48:13 CET 2007Frontiers of WWW Research and Development - APWeb 2006733--738Cubic Analysis of Social Bookmarking for Personalized Recommendation2006bookmarking collaborative filtering recommendersystems social tagging 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 -SpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/25fbd24f07fe8784b516e69b0eb3192f3/hothohotho2007-11-16T11:39:18+01:00bookmarking folksonomy kdubiq recommender social summerschool tagging taggingsurvey toread <span class="authorEditorList"><a href="/author/Xu">Yanfei Xu</a>, <a href="/author/Zhang">Liang Zhang</a>, and <a href="/author/Liu">Wei Liu</a> </span><em>Frontiers of WWW Research and Development - APWeb 2006</em> (<em>2006</em>)Fri Nov 16 11:39:18 CET 2007Frontiers of WWW Research and Development - APWeb 2006733--738Cubic Analysis of Social Bookmarking for Personalized Recommendation2006bookmarking folksonomy kdubiq recommender social summerschool tagging taggingsurvey toread 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 -SpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/2a74b4b245be489e59aa31fc9a133c084/dblpdblp2005-12-20T00:00:00+01:00dblp <span class="authorEditorList"><a href="/author/Xu">Yanfei Xu</a>, <a href="/author/Zhang">Liang Zhang</a>, and <a href="/author/Liu">Wei Liu</a> </span><em>APWeb, </em><em>volume 3841 of Lecture Notes in Computer Science, </em><em>page 733-738. </em><em>Springer, </em>(<em>2006</em>)Tue Dec 20 00:00:00 CET 2005APWebconf/apweb/2006733-738Lecture Notes in Computer ScienceCubic Analysis of Social Bookmarking for Personalized Recommendation.38412006dblp dblp