PUMA publications for /author/Steve%20Cayzer/recommender%20toreadhttps://puma.uni-kassel.de/author/Steve%20Cayzer/recommender%20toreadPUMA RSS feed for /author/Steve%20Cayzer/recommender%20toread2024-03-28T09:09:37+01:00Personalized 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.