PUMA publications for /author/Hui%20Wanhttps://puma.uni-kassel.de/author/Hui%20WanPUMA RSS feed for /author/Hui%20Wan2024-03-28T17:46:26+01:00Personalized Tag Recommendations via Tagging and Content-based Similarity Metricshttps://puma.uni-kassel.de/bibtex/2157846898c1c2a65c265a913ebac115a/jaeschkejaeschke2008-10-16T17:21:28+02:00tagging similarity recommender tag content <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andrew Byde" itemprop="url" href="/author/Andrew%20Byde"><span itemprop="name">A. Byde</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hui Wan" itemprop="url" href="/author/Hui%20Wan"><span itemprop="name">H. Wan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steve Cayzer" itemprop="url" href="/author/Steve%20Cayzer"><span itemprop="name">S. Cayzer</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the International Conference on Weblogs and Social
Media</span>, </em></span>(<em><span>März 2007<meta content="März 2007" itemprop="datePublished"/></span></em>)Thu Oct 16 17:21:28 CEST 2008Proceedings of the International Conference on Weblogs and Social
MediaMarchPersonalized Tag Recommendations via Tagging and Content-based Similarity Metrics2007tagging similarity recommender tag content This short paper describes a novel technique for generating personalized
tag recommendations for users of social book- marking sites such
as del.icio.us. Existing techniques recom- mend tags on the basis
of their popularity among the group of all users; on the basis of
recent use; or on the basis of simple heuristics to extract keywords
from the url being tagged. Our method is designed to complement these
approaches, and is based on recommending tags from urls that are
similar to the one in question, according to two distinct similarity
metrics, whose principal utility covers complementary cases.Personalized Tag Recommendations via Tagging and Content-based Similarity Metricshttps://puma.uni-kassel.de/bibtex/2157846898c1c2a65c265a913ebac115a/hothohotho2007-11-16T20:46:39+01:00tagging taggingsurvey recommender collaborative social filtering bookmarking toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andrew Byde" itemprop="url" href="/author/Andrew%20Byde"><span itemprop="name">A. Byde</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hui Wan" itemprop="url" href="/author/Hui%20Wan"><span itemprop="name">H. Wan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steve Cayzer" itemprop="url" href="/author/Steve%20Cayzer"><span itemprop="name">S. Cayzer</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the International Conference on Weblogs and Social Media</span>, </em></span>(<em><span>März 2007<meta content="März 2007" itemprop="datePublished"/></span></em>)Fri Nov 16 20:46:39 CET 2007Proceedings of the International Conference on Weblogs and Social MediaMarchPersonalized Tag Recommendations via Tagging and Content-based Similarity Metrics2007tagging taggingsurvey recommender collaborative social filtering bookmarking toread This short paper describes a novel technique for generating personalized tag recommendations for users of social book- marking sites such as del.icio.us. Existing techniques recom- mend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.