| | Article: "The Race to Create a 'Smart' Google: Everything you buy online says a little bit about you. And if all those bits get put into one big trove of data about you and your tastes? Marketer's heaven," by Jeffrey O'Brien of Fortune Magazine (November 20 2006). Discusses the growing business of recommender systems and how we are moving from search (using search engines to find what you know already exists) to discovery (where you learn about things you did not know existed). | http://money.cnn.com/magazines/fortune/fortune_archive/2006/11/27/8394347/index.htm
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| | Article: "Recommender Systems" by Paul Resnick of AT&T Labs-Research, and Hal R. Varian from the School of Information Management and Systems at the University of California, Berkeley. Discusses what recommender systems are, gives a brief history, looks at the social implications and the types of business models. | http://www.acm.org/pubs/cacm/MAR97/resnick.html
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| | An introduction to recommender systems. Topics include the weaknesses of recommender systems (data sparseness, cold start, attacks on recommender systems, and understanding and controlling recommender systems) and solving the weaknesses (trust awareness). | http://moloko.itc.it/paoloblog/papers/itrust2004/node1.html
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| | Wikipedia entry for collaborative filtering. Discusses the methodology, history, types (active filtering, passive filtering, and item-based filtering) and applications (for both commercial and non-commercial uses). Includes links to collaborative software products. | http://en.wikipedia.org/wiki/Collaborative_filtering
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| | Wikipedia entry for recommender systems. Recommender systems collect data using collaborative filtering systems to determine users' tastes and interests as they search the Internet. Includes an introduction to recommender systems, a list of popular recommender systems and links to a couple of research papers. | http://en.wikipedia.org/wiki/Recommender_systems
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