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Titlebook: Recommender Systems Handbook; Francesco Ricci,Lior Rokach,Bracha Shapira Book 20152nd edition Springer Science+Business Media New York 201

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发表于 2025-3-21 17:12:27 | 显示全部楼层 |阅读模式
书目名称Recommender Systems Handbook
编辑Francesco Ricci,Lior Rokach,Bracha Shapira
视频video
概述Includes major updates as well as 20 new chapters.Presents detailed case studies.Shares tips and insights from renowned experts in the field
图书封面Titlebook: Recommender Systems Handbook;  Francesco Ricci,Lior Rokach,Bracha Shapira Book 20152nd edition Springer Science+Business Media New York 201
描述This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included.In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender system
出版日期Book 20152nd edition
关键词Collaborative filtering; Collective intelligence; Context-aware systems; Data mining; Data science; Decis
版次2
doihttps://doi.org/10.1007/978-1-4899-7637-6
isbn_softcover978-1-4899-7780-9
isbn_ebook978-1-4899-7637-6
copyrightSpringer Science+Business Media New York 2015
The information of publication is updating

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发表于 2025-3-21 21:03:57 | 显示全部楼层
发表于 2025-3-22 01:20:45 | 显示全部楼层
The Anatomy of Mobile Location-Based Recommender Systemsrecommending venues, and the techniques that researchers have used to evaluate the quality of these recommendations, using research that is sourced from a variety of fields. This chapter closes by highlighting a number of opportunities and open challenges related to building future mobile recommender systems.
发表于 2025-3-22 05:57:36 | 显示全部楼层
Advances in Collaborative Filterings that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.
发表于 2025-3-22 11:25:51 | 显示全部楼层
Evaluating Recommender Systems with User Experimentsments, covering the following topics: formulating hypotheses, sampling participants, creating experimental manipulations, measuring subjective constructs with questionnaires, and statistically evaluating the results.
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发表于 2025-3-22 19:22:53 | 显示全部楼层
Data Mining Methods for Recommender Systemsnd Support Vector Machines. We describe the .-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.
发表于 2025-3-23 00:29:39 | 显示全部楼层
发表于 2025-3-23 02:23:22 | 显示全部楼层
Book 20152nd editiontheories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included.In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems
发表于 2025-3-23 05:55:03 | 显示全部楼层
Recommender Systems in Industry: A Netflix Case Studyrom the Netflix Prize. We will then use Netflix personalization as a case study to describe several approaches and techniques used in a real-world recommendation system. Finally, we will pinpoint what we see as some promising current research avenues and unsolved problems that deserve attention in this domain from an industry perspective.
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