Recommender Systems Handbook下载

1努力加油1 2019-03-05 06:32:47

一本很好的学习推荐系统的参考手册，全面而系统。由全球做推荐系统的研究人员参与编写。目录主干：
1 Introduction to Recommender Systems Handbook
Part I Basic Techniques
2 Data Mining Methods for Recommender Systems
3 Content-based Recommender Systems: State of the Art and Trends
4 A Comprehensive Survey of Neighborhood-based
Recommendation Methods

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一本很好的学习推荐系统的参考手册，全面而系统。由全球做推荐系统的研究人员参与编写。目录主干： 1 Introduction to Recommender Systems Handbook Part I Basic Techniques 2 Data Mining Methods for Recommender Systems 3 Content-based Recommender Systems: State of the Art and Trends 4 A Comprehensive Survey of Neighborhood-based Recommendation Methods 5 Advances in Collaborative Filtering 6 Developing Constraint-based Recommenders 7 Context-Aware Recommender Systems Part II Applications and Evaluation of RSs 8 Evaluating Recommendation Systems 9 A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment. 10 How to Get the Recommender Out of the Lab? 11 Matching Recommendation Technologies and Domains 12 Recommender Systems in Technology Enhanced Learning Part III Interacting with Recommender Systems 13 On the Evolution of Critiquing Recommenders 14 Creating More Credible and Persuasive Recommender Systems:The Influence of Source Characteristics on Recommender System Evaluations 15 Designing and Evaluating Explanations for Recommender Systems 16 Usability Guidelines for Product Recommenders Based on Example Critiquing Research 17 Map Based Visualization of Product Catalogs Part IV Recommender Systems and Communities 18 Communities, Collaboration, and Recommender Systems in PersonalizedWeb Search 19 Social Tagging Recommender Systems 20 Trust and Recommendations 21 Group Recommender Systems: Combining Individual Models Part V Advanced Algorithms 22 Aggregation of Preferences in Recommender Systems 23 Active Learning in Recommender Systems 24 Multi-Criteria Recommender Systems 25 Robust Collaborative Recommendation

The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

Introduction to Recommender Systems Handbook Francesco Ricci, Lior Rokach and Bracha Shapira Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal

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