PUMA publications for /author/Jon%20Herlockerhttps://puma.uni-kassel.de/author/Jon%20HerlockerPUMA RSS feed for /author/Jon%20Herlocker2024-03-28T21:32:43+01:00Collaborative Filtering Recommender Systemshttps://puma.uni-kassel.de/bibtex/21c611c2e32fb3b735c3adcd413e95201/jaeschkejaeschke2008-12-19T15:12:21+01:00cf recommender collaborative filtering webzu <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. Ben Schafer" itemprop="url" href="/author/J.%20Ben%20Schafer"><span itemprop="name">J. Schafer</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dan Frankowski" itemprop="url" href="/author/Dan%20Frankowski"><span itemprop="name">D. Frankowski</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jon Herlocker" itemprop="url" href="/author/Jon%20Herlocker"><span itemprop="name">J. Herlocker</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Shilad Sen" itemprop="url" href="/author/Shilad%20Sen"><span itemprop="name">S. Sen</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">The Adaptive Web: Methods and Strategies of Web Personalization</span>, </em><em>Volume 4321 von Lecture Notes in Computer Science, </em><em>Kapitel 9, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin, Heidelberg, </em></span>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Fri Dec 19 15:12:21 CET 2008Berlin, HeidelbergThe Adaptive Web: Methods and Strategies of Web Personalization9291-324Lecture Notes in Computer ScienceCollaborative Filtering Recommender Systems43212007cf recommender collaborative filtering webzu One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.