| Document Date: 2013-02-14 03:12:07
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Company Springer Science+Business Media B.V. / ACM Recommender Systems / Netflix / Pearson / 123 Recommender / Agents Inc. / Amazon.com / MAE / / / Event Person Communication and Meetings / / Facility University of Minnesota / / IndustryTerm recommendation algorithms / early research recommender systems / collaborative filtering recommender systems / data mining researchers / human-centered computing / telephone sales marketing applications / real-time use / earlier agent-based systems / recommender systems / automated collaborative filtering systems / live systems / signal processing / earliest retail sales applications / item algorithm / collaborative web search tool / recommender algorithms / collaborative filtering algorithms / similar algorithms / software / singular value decomposition algorithm / online retailers / ecommerce stores / user algorithm / content applications / faster online response time / prediction algorithms / social networks / k-nearest-neighbor prediction algorithm / classic recommender algorithm / recent algorithms / / Movie Star Wars / Citizen Kane / The Aviator / / NaturalFeature Video Recommender Hill / / Organization University of Minnesota / Minneapolis / Department of Computer Science and Engineering / / Person Joseph A. Konstan / John Riedl / Chen / Pearl Pu / Ali Stam / Van Stam / / Position Harper / same author / supervisor / word processor / rich user model for them / / Technology recommendation algorithms / user algorithm / 1.2 Recommender algorithms / underlying algorithms / collaborative filtering algorithms / k-nearest-neighbor prediction algorithm / machine learning / singular value decomposition algorithm / prediction algorithms / recommender algorithms / evaluating prediction algorithms / item algorithm / six algorithms / alternative algorithm / data mining / classic recommender algorithm / virtual community / /
SocialTag |
User Model User-Adap Inter[removed]:101–123 DOI[removed]s11257[removed]x ORIGINAL PAPER Recommender systems: from algorithms to user experience