Recommender Systems Handbook

ISBN-13: 9781489977809 / Angielski / Miękka / 2016 / 1003 str.

Francesco Ricci; Lior Rokach; Bracha Shapira

Recenzje

"If you have time for just one book to get yourself up to speed with the latest and best in recommender systems, this is the book you want. ... this is an excellent educational resource on the main techniques employed for making recommendations ... . is definitely a book to read to get updated on the state of the art of recommender systems, and also to get a feel of the breadth of the research areas available in this area." (Jun-Ping Ng, Computing Reviews, April, 2016)

Spis treści

Recommender Systems: Introduction and Challenges.- A Comprehensive Survey of Neighborhood-based Recommendation Methods.- Advances in Collaborative Filtering.- Semantics-aware Content-based Recommender Systems.- Constraint-based Recommender Systems.- Context-Aware Recommender Systems.- Data Mining Methods for Recommender Systems.- Evaluating Recommender Systems.- Evaluating Recommender Systems with User Experiments.- Explaining Recommendations: Design and Evaluation.- Recommender Systems in Industry: A Netflix Case Study.- Panorama of Recommender Systems to Support Learning.- Music Recommender Systems.- The Anatomy of Mobile Location-Based Recommender Systems.- Social Recommender Systems.- People-to-People Reciprocal Recommenders.- Collaboration, Reputation and Recommender Systems in Social Web Search.- Human Decision Making and Recommender Systems.- Privacy Aspects of Recommender Systems.- Source Factors in Recommender System Credibility Evaluation.- Personality and Recommender Systems.- Group Recommender Systems: Aggregation, Satisfaction and Group Attributes.- Aggregation Functions for Recommender Systems.- Active Learning in Recommender Systems.- Multi-Criteria Recommender Systems.- Novelty and Diversity in Recommender Systems.- Cross-domain Recommender Systems.- Robust Collaborative Recommendation.

O autorze

Francesco Ricci is a professor of computer science at the Free University of Bozen-Bolzano, Italy. His current research interests include recommender systems, intelligent interfaces, mobile systems, machine learning, case-based reasoning, and the applications of ICT to health and tourism. He has published more than one hundred thirty of academic papers on these topics. He is the editor in chief of the Journal of Information Technology & Tourism and on the editorial board of User Modeling and User Adapted Interaction. Lior Rokach is a data scientist and an associate professor of information systems and software engineering at Ben-Gurion University of the Negev (BGU). Rokach established the machine learning laboratory in BGU which promotes innovative adaptations of machine learning and data mining methods to create the next generation of intelligent systems. Rokach is known for his contributions to the advancement of machine learning, recommender systems and cyber security. Bracha Shapira is an associate professor and the head of the information systems and engineering Department at Ben-Gurion University of the Negev (BGU). She leads large scale research projects at the Telekom Innovation Laboratories at BGU in the area of data analytics, recommender systems and personalization that delivers innovative technologies to address challenges in these fields. Shapira is known for her contribution in integrating social network, context awareness and privacy consideration to recommender systems.