Recommender Systems Handbook

Recommender Systems Handbook
PDF
  • eBook:
    Recommender Systems Handbook
  • Author:
    Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor
  • Edition:
    2011 edition
  • Categories:
  • Data:
    October 28, 2010
  • ISBN:
    0387858199
  • ISBN-13:
    9780387858197
  • Language:
    English
  • Pages:
    842 pages
  • Format:
    PDF

Book Description
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.

Content

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

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