Supporting the user with the decision-making and buying process, recommender systems have proven to be a valuable means for online users to cope with the virtual information overload. It is one of the most powerful and popular tools in electronic commerce available today.
Development of recommender systems is a multi-disciplinary effort, involving experts from various fields such as data mining, artificial intelligence, statistics, human computer interaction, information retrieval/technology, and adaptive user interfaces. This book covers all aspects and important techniques for recommender systems, such as collaborative filtering, content based techniques, popular hybrid approaches and a detailed tutorial of recommender systems software.

Designed for industry researchers in the fields of information technology, e-commerce, information retrieval, data mining, databases and statistics, and practitioners, this book is also suitable for advanced-level students in computer science as a secondary textbook.

Preface.- Foundation. Introduction to Recommender Systems. Useful AI Methods for Recommender Systems. Challenges in Recommender Systems. Evaluation of Recommender Systems.- Techniques. Collaborative Filtering Techniques. Content-Based Techniques. Knowledge-Based Techniques. Demographic Techniques. Community Based Recommender Systems. Hybrid Techniques. PERES - A Workbench for Recommender Systems.- Advances in Recommender Systems. Explanations in Recommender Systems. Stereotype-based Recommender Systems. Security and Trust in Recommender Systems. Elicitation of User Preferences. Ontologies and Semantic Web Technologies for Recommender Systems.- Index.