Recommender Systems are software tools and techniques providing suggestions for
items to be of use to a user. The suggestions provided are aimed at supporting their
users in various decision-making processes, such as what items to buy, what music
Development of recommender systems is a multi-disciplinary effort which involves
experts from various fields such as Artificial intelligence, Human Computer
Interaction, Information Technology, Data Mining, Statistics, Adaptive User Interfaces,
Decision Support Systems, Marketing, or Consumer Behavior. Recommender
Systems Handbook: A Complete Guide for Research Scientists and Practitioners
aims to impose a degree of order upon this diversity by presenting a coherent and
unified repository of recommender systems’ major concepts, theories, methodologies,
trends, challenges and applications. This is the first comprehensive book which
is dedicated entirely to the field of recommender systems and covers several aspects
of the major techniques. Its informative, factual pages will provide researchers, stuclassical
methods, as well as extensions and novel approaches that were recently introduced.
The book consists of five parts: techniques, applications and evaluation of
recommender systems, interacting with recommender systems, recommender systems
and communities, and advanced algorithms. The first part presents the most
popular and fundamental techniques used nowadays for building recommender systems,
such as collaborative filtering, content-based filtering, data mining methods
and context-aware methods. The second part starts by surveying techniques and approaches
that have been used to evaluate the quality of the recommendations. Then
deals with the practical aspects of designing recommender systems, it describes design
and implementation consideration, setting guidelines for the selection of the
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to listen, or what news to read. Recommender systems have proven to be valuable
means for online users to cope with the information overload and have
Correspondingly, various techniques for recommendation generation have been
proposed and during the last decade, many of them have also been successfully
deployed in commercial environments.
