Recommender Systems Handbook

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.

Table of Contents

Chapter 1 Introduction to Recommender Systems Handbook

Part I Basic Techniques
Chapter 2 Data Mining Methods for Recommender Systems
Chapter 3 Content-based Recommender Systems: State of the Art and Trends
Chapter 4 A Comprehensive Survey of Neighborhood-based Recommendation Methods
Chapter 5 Advances in Collaborative Filtering
Chapter 6 Developing Constraint-based Recommenders
Chapter 7 Context-Aware Recommender Systems

Part II Applications and Evaluation of RSs
Chapter 8 Evaluating Recommendation Systems
Chapter 9 A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment
Chapter 10 How to Get the Recommender Out of the Lab?
Chapter 11 Matching Recommendation Technologies and Domains
Chapter 12 Recommender Systems in Technology Enhanced Learning

Part III Interacting with Recommender Systems
Chapter 13 On the Evolution of Critiquing Recommenders
Chapter 14 Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender SystemEvaluations
Chapter 15 Designing and Evaluating Explanations for Recommender Systems
Chapter 16 Usability Guidelines for Product Recommenders Based on Example Critiquing Research
Chapter 17 Map Based Visualization of Product Catalogs

Part IV Recommender Systems and Communities
Chapter 18 Communities, Collaboration, and Recommender Systems in PersonalizedWeb Search
Chapter 19 Social Tagging Recommender Systems
Chapter 20 Trust and Recommendations
Chapter 21 Group Recommender Systems:Combining Individual Models

Part V Advanced Algorithms
Chapter 22 Aggregation of Preferences in Recommender Systems
Chapter 23 Active Learning in Recommender Systems
Chapter 24 Multi-Criteria Recommender Systems
Chapter 25 Robust Collaborative Recommendation