Recommender Systems Handbook / 
edited by Francesco Ricci, Lior Rokach, Bracha Shapira. 
 - 2nd ed. 2015. 
 - Boston, Mass. :   Harvard Business Review Press,    2019 
 - 1 online resource (XVII, 1003 pages 144 illustrations, 78 illustrations in color.)   cm. 
<br/><br/> This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems' major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems. 
<br/><br/><label>ISBN: </label>9781489976376 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Information storage and retrieval.<br/>Artificial intelligence.<br/>Information Storage and Retrieval.<br/>Artificial Intelligence.
<br/><br/><label>Subjects--Index Terms: </label>Trung tâm thông tin Lưu trữ và truy xuất thông tin 
<br/><br/><label>Dewey Class. No.: </label>025.04  / BUC