Building Effective Recommender Systems

Recommender systems support the user with the decision-making and buying process. The explosive growth of e-commerce environments has made the issue of information overload increasingly serious. Recommender systems have proven to be a valuable means for online users to cope with the virtual information overload, and 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. Building Effective Recommender Systems is the first comprehensive book which is dedicated entirely to the field of recommender systems. 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.
Building Effective Recommender Systems is designed for researchers in the fields of information technology, e-commerce, information retrieval, data mining, databases and statistics, and practitioners that work for well known corporations such as Amazon, Google, Microsoft and AT&&T. This book is also suitable for advanced-level students in computer science as a secondary textbook.