Titlebook: Recommender Systems Handbook; Francesco Ricci,Lior Rokach,Paul B. Kantor Book 20111st edition Springer-Science+Business Media, LLC 2011 Co

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https://doi.org/10.1007/978-0-387-85820-3Collaborative filtering; Content-based filtering; Information access; Personalization; currentsmp

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Springer-Science+Business Media, LLC 2011

美麗的寫

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Introduction to Recommender Systems Handbook,iefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.

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Content-based Recommender Systems: State of the Art and Trendson systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (i

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Advances in Collaborative Filteringin the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extension