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Recommender Systems: An Introduction Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich
Publisher: Cambridge University Press

The purpose of this post is to explain how to use Apache Mahout to deploy a massively scalable, high throughput recommender system for a certain class of usecases. Three specific problems can be distinguished for content-based filtering: Content description. In domains where the items consist of music or video However, collaborative filtering does introduce certain problems of its own: Early rater problem. In some domains generating a useful description of the content can be very difficult. The paper you link deals strictly with the latter. Recommendation systems: privacy and interactivity. The book is a very helpful introduction for all researcher that want to conduct research on personalization, learner support and knowledge management through recommender systems. Earlier this month, Netflix (an American provider of on-demand Internet streaming media) offered some details about the working of its recommendation system. Talks that stood out most for me were Barry Smyth's introduction to the state-of-the-art on recommender systems and Pádraig Cunnigham's similar introduction to the Clique cluster's work on social network analysis. Nudging Serendipity – Guiding users toward discovery of unknown unknowns. Based on automated collaborative filtering, these recommender systems were introduced, refined, and commercialized by the team at GroupLens. Both content-based filtering and collaborative filtering have there strengths and weaknesses. As for the former perhaps the following would be more useful: http://paloalto.thlab.net/publications/80.