Recommender Systems Handbook / edited by Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor.

Format:

Book

Contributor:

Ricci, Francesco, editor.

Rokach, Lior, editor.

Shapira, Bracha, editor.

Kantor, Paul B., editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Information storage and retrieval.

Data mining.

Electronic commerce.

User interfaces (Computer systems).

Database management.

Artificial Intelligence.

Information Storage and Retrieval.

Data Mining and Knowledge Discovery.

e-Commerce/e-business.

User Interfaces and Human Computer Interaction.

Database Management.

Local Subjects:

Artificial Intelligence.

Information Storage and Retrieval.

Data Mining and Knowledge Discovery.

e-Commerce/e-business.

User Interfaces and Human Computer Interaction.

Database Management.

Physical Description:

1 online resource (XXX, 842 pages) : 20 illustrations

Edition:

First edition 2011.

Contained In:

Springer eBooks

Place of Publication:

New York, NY : Springer US : Imprint: Springer, 2011.

System Details:

text file PDF

Summary:

The explosive growth of e-commerce and online environments has made the issue of 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 one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the 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 illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

Contents:

Introduction to Recommender Systems Handbook

Part I Basic Techniques

Data Mining Methods for Recommender Systems

Content-based Recommender Systems: State of the Art and Trends

A Comprehensive Survey of Neighborhood-based Recommendation Methods

Advances in Collaborative Filtering

Developing Constraint-based Recommenders

Context-Aware Recommender Systems

Part II Applications and Evaluation of RSs

Evaluating Recommendation Systems

A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment

How to Get the Recommender Out of the Lab?

Matching Recommendation Technologies and Domains

Recommender Systems in Technology Enhanced Learning

Part III Interacting with Recommender Systems

On the Evolution of Critiquing Recommenders

Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender System Evaluations

Designing and Evaluating Explanations for Recommender Systems

Usability Guidelines for Product Recommenders Based on Example Critiquing Research

Map Based Visualization of Product Catalogs

Part IV Recommender Systems and Communities

Communities, Collaboration, and Recommender Systems in Personalized Web Search

Social Tagging Recommender Systems

Trust and Recommendations

Group Recommender Systems: Combining Individual Models

Aggregation of Preferences in Recommender Systems

Active Learning in Recommender Systems

Multi-Criteria Recommender Systems

Robust Collaborative Recommendation

Index.

Other Format:

Printed edition:

ISBN:

978-0-387-85820-3

9780387858203

Access Restriction:

Restricted for use by site license.