Recommender systems handbook

The explosive growth of e-commerce and online environments has made the issueof 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 oneof the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. Duringthe 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 supports the user in decision-making, planning and purchasing processes who work forwell known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is also suitable for researchers and advanced-level students in computer science as a reference. “First comprehensive handbook which is dedicated entirely to the field of recommender systems IT professionals that provides services and products to the end-customers via the Internet or other communication means, will find this book very valuable, because it contains detailed algorithms and provides a Java source for all algorithms INDICE: Preface.- Foundation.- Introduction to Recommender Systems.- Useful AI MR methods for Recommender Systems.- Challenges for Recommender Systems.-Evaluation of Recommender Systems.- Ranking and learning.- Techniques.- Collaborative Filtering Techniques.- Content-Based Techniques.- Knowledge-Based Techniques.- Demographic Techniques.- Conversation Management.- Community Based Recommender Systems.- Hybrid Techniques.- Visualization of Recommendations.- Group Recommendation.- Explanation of Recommendations.- Advances in Recommender Systems.- Stereotype-based Recommender Systems .- Security, Privacy and Trust in Recommender Systems .- Elicitation of User Preferences.- Ontologies and Semantic Web Technologies for Recommender Systems.- Recommendations and Search Engines.- Applications.- Application in Tourism.- Mobile Recommenders.- News Recommendations.- Multimedia Recommendations.- Index.

• ISBN: 978-0-387-85819-7
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 780
• Fecha Publicación: 01/01/2011
• Nº Volúmenes: 1
• Idioma: Inglés