Welcome to the Recommender Systems Resource Center. Recommender systems collect data using collaborative filtering systems to determine users' tastes and interests as they search the Internet. Sites can gather information about your personal interests, compare your information to other users with similar interests and make recommendations such as movies you might enjoy, the next book you should read, etc. This resource centers includes some popular recommender systems, tools for adding this functionality to your sites, research papers and more. In the Recommender Systems Resource Center you'll find links to:

• Popular recommender systems including Pandora™, Netflix, Whattorent.com, CleverSet, ChoiceStream, MyStrands, StumbleUpon, Last.fm, MovieLens and others.
• CoFE (the COllaborative Filtering Engine)—a free, open source recommendation engine for collaborative filtering.
• The Recommender-CRM Personalization Engine—a free, open source recommender system that you can use on your site that will make personalized product recommendations to your site visitors.
• Wikipedia entries for recommender systems and collaborative filtering.
• The Recommenders06 blog where you'll find discussion about recommender systems including attacks, evaluation, infrastructure, music recommendations, personalization, and more.
• The blog entry, "Pandora and Last.fm: Nature vs. Nurture in Music Recommenders," by Steve Krause, VP of Analytic Products at CNET Channel.
• Recommender.org—a free, open source recommender framework built in C# and ASP.NET.
• IUI 2007 (International Conference of Intelligent User Interfaces) and The 30th Annual International ACM SIGIR Conference, both of which include sessions on recommender systems.
• The Recommender Systems newsgroup on Yahoo! Groups.
• The sample Chapter, "The Insider's Guide to Collaborative Filtering and Recommender Systems," from Word of Mouse: The Marketing Power of Collaborative Filtering, August 2002, by John Reidl, Joseph Konstan, and Eric Vrooman.
• Papers that discuss the history, weaknesses and social implications of recommender systems.
• The article, "The Race to Create a 'Smart' Google," by Jeffrey O'Brien of Fortune Magazine.
• The webcast, "Recommender Systems," by Joan Silvi.
• The paper, "Beyond Recommender Systems: Helping People Help Each Other," by Loren Terveen and Will Hill (AT&T Labs Research).
• The paper, "Interaction Design for Recommender Systems," by Kirsten Swearingham and Rashmi Sinha.
• The article, "Building a Lifestyle Recommender System," by Supiya Ujjin and Peter J. Bentley of the University College London.
• The research Paper, "Using Trust in Recommender Systems: an Experimental Analysis," by Paolo Massa1 and Bobby Bhattacharjee of the University of Trento and the University of Maryland.
• The paper, "Analysis of Recommender Systems’ Algorithms," by Emmanouil Vozalis and Konstantinos G. Margaritis.
• The paper, "Semantic Web Interaction through Trust Network Recommender Systems," by Jennifer Golbeck of the University of Maryland.
• Whitepaper: "Evaluating Collaborative Filtering Recommender Systems," by Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl.
• Information about the GroupLens Research team from the Department of Computer Science and Engineering at the University of Minnesota. GroupLens studies recommender systems, collaborative filtering, online communities and more.