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Recommended Systems Resource Center

 

Recommender Systems Tutorials and Webcasts
"Is Seeing Believing?"
http://www.win.tue.nl/~laroyo/2L340/resources/recommender-systems-customer-attitudes.pdf
Tutorial: "Is Seeing Believing? How Recommender Interfaces Affect Users' Opinions," by Dan Cosley, Shyong K. Lam, Istvan Albert, Joseph A. Konstan, and John Riedl. Discusses how recommendations may influence user ratings, mapping opinions to ratings, recommender systems, ratings consistency, tricking recommender's and influencing users, design of rating scales, conformity and persuasive computing, how do different rating scales affect users' ratings, and do users notice when predictions are manipulated.
"Amazon.com Recommendations"
http://www.win.tue.nl/~laroyo/2L340/resources/Amazon-Recommendations.pdf
Tutorial: "Amazon.com Recommendations: Item-to-Item Collaborative Filtering," by Greg Linden, Brent Smith and Jeremy Smith. Discusses recommendation algorithms, traditional collaborative filtering, cluster models, search-based methods, item-to-item collaborative filtering (how it works), and scalability.
"AI Techniques for Personal Recommendation"
http://www.win.tue.nl/~laroyo/2L340/resources/tutorial_recommendations.pdf
Tutorial: "AI Techniques for Personal Recommendation" by Joseph Konstan, John Riedl and Anthony Jameson. Discusses application space, filtering techniques, collaborative filtering issues, AI techniques, privacy, consumers vs. marketers, recommendations unplugged, recommender systems status, commercial tools, what are recommender systems, history of recommender systems, and recommender application space.
"Recommender Systems"
http://www.iota.org/Winter99/recommend.html
Webcast: "Recommender Systems" by Joan Silvi. Discusses what recommender systems are and how they work, and talks with Paul Resnick, a professor at the University of Michigan's School of information, who has done extensive research on recommender systems. A text version of the interview is also available.

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Update :: December 18, 2017