| 書目名稱 | Recommender Systems Handbook | | 編輯 | Francesco Ricci,Lior Rokach,Bracha Shapira | | 視頻video | http://file.papertrans.cn/825/824122/824122.mp4 | | 概述 | Includes eleven new chapters in this third edition handbook.Introduces advanced topics such as Deep Neural Networks and RS, Natural Language Processing and RS and more.Chapters are written by major re | | 圖書封面 |  | | 描述 | .This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender? systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods.?.The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the an | | 出版日期 | Book 2022Latest edition | | 關(guān)鍵詞 | Collaborative filtering; Collective intelligence; Context-aware systems; Data mining; Data science; Decis | | 版次 | 3 | | doi | https://doi.org/10.1007/978-1-0716-2197-4 | | isbn_softcover | 978-1-0716-2199-8 | | isbn_ebook | 978-1-0716-2197-4 | | copyright | Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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