PUMA publications for /author/Liang%20Zhanghttps://puma.uni-kassel.de/author/Liang%20ZhangPUMA RSS feed for /author/Liang%20Zhang2024-03-29T16:39:40+01:00Cubic Analysis of Social Bookmarking for Personalized Recommendationhttps://puma.uni-kassel.de/bibtex/298dd99b5f4189c8427163fd5a7568e1d/jaeschkejaeschke2008-11-05T11:19:04+01:00recommender tag folksonomy <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yanfei Xu" itemprop="url" href="/author/Yanfei%20Xu"><span itemprop="name">Y. Xu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Liang Zhang" itemprop="url" href="/author/Liang%20Zhang"><span itemprop="name">L. Zhang</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wei Liu" itemprop="url" href="/author/Wei%20Liu"><span itemprop="name">W. Liu</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">APWeb</span>, </em></span><em>Volume 3841 von Lecture Notes in Computer Science, </em><em>Seite <span itemprop="pagination">733--738</span>. </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></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 Recommendation38412006recommender tag folksonomy 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 -Cubic Analysis of Social Bookmarking for Personalized Recommendationhttps://puma.uni-kassel.de/bibtex/25fbd24f07fe8784b516e69b0eb3192f3/hothohotho2007-11-16T11:39:18+01:00tagging taggingsurvey recommender summerschool social folksonomy bookmarking kdubiq toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yanfei Xu" itemprop="url" href="/author/Yanfei%20Xu"><span itemprop="name">Y. Xu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Liang Zhang" itemprop="url" href="/author/Liang%20Zhang"><span itemprop="name">L. Zhang</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wei Liu" itemprop="url" href="/author/Wei%20Liu"><span itemprop="name">W. Liu</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Frontiers of WWW Research and Development - APWeb 2006</em></span></span> </span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Fri Nov 16 11:39:18 CET 2007Frontiers of WWW Research and Development - APWeb 2006733--738Cubic Analysis of Social Bookmarking for Personalized Recommendation2006tagging taggingsurvey recommender summerschool social folksonomy bookmarking kdubiq 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 usersâ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods. ER -SpringerLink - Book Chapter