Recommender Systems
An Introduction

This book introduces different approaches to developing recommender systems that automate choice-making strategies to provide affordable, personal, and high-quality recommendations.

Dietmar Jannach (Author), Markus Zanker (Author), Alexander Felfernig (Author), Gerhard Friedrich (Author)

9780521493369, Cambridge University Press

Hardback, published 30 September 2010

352 pages, 72 b/w illus. 29 tables
23.1 x 15.5 x 2.5 cm, 0.6 kg

'This book is the most comprehensive and useful introductory text on recommender systems yet published. Anyone who teaches in this area will be pleased to have, at last, one source for all of the essential topics. Especially noteworthy are the book's broad coverage of different types of recommenders and its thorough treatment of evaluation. It is billed as an introduction, but I think that even experienced practitioners will find new and interesting material here.' Robin Burke

In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

1. Introduction
Part I. Introduction into Basic Concepts: 2. Collaborative recommendation
3. Content-based recommendation
4. Knowledge-based recommendation
5. Hybrid recommendation approaches
6. Explanations in recommender systems
7. Evaluating recommender systems
8. Case study - personalized game recommendations on the mobile Internet
Part II. Recent Developments: 9. Attacks on collaborative recommender systems
10. Online consumer decision making
11. Recommender systems and the next-generation Web
12. Recommendations in ubiquitous environments
13. Summary and outlook.

Subject Areas: Artificial intelligence [UYQ], Data mining [UNF], Digital TV & media centres: consumer/user guides [UDV], Internet guides & online services [UDB]