Recommender systems handbook (eBook, 2015) [WorldCat.org]
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Recommender systems handbook

Author: Francesco Ricci; Lior Rokach; Bracha Shapira
Publisher: New York : Springer, [2015] ©2015
Edition/Format:   eBook : Document : English : Second editionView all editions and formats
Summary:
This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systemsℓ́ℓ 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  Read more...
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Details

Genre/Form: Electronic books
Additional Physical Format: Printed edition:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Francesco Ricci; Lior Rokach; Bracha Shapira
ISBN: 9781489976376 148997637X 1489976361 9781489976369
OCLC Number: 929952078
Description: 1 online resource (xvii, 1003 pages) : illustrations
Contents: 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.
Responsibility: Francesco Ricci, Lior Rokach, Bracha Shapira, editors.

Abstract:

This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systemsℓ́ℓ 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.

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"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 Read more...

 
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