Recommender Systems: An Introduction

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.

Table of Contents

Chapter 1 Introduction

PART I: Introduction to basic concepts
Chapter 2 Collaborative Recommendation
Chapter 3 Content-Based Recommendation
Chapter 4 Knowledge-Based Recommendation
Chapter 5 Hybrid Recommendation Approaches
Chapter 6 Explanations In Recommender Systems
Chapter 7 Evaluating Recommender Systems
Chapter 8 Case Study: Personalized Game Recommendations On The Mobile Internet

PART II: Recent developments
Chapter 9 Attacks On Collaborative Recommender Systems
Chapter 10 Online Consumer Decision Making
Chapter 11 Recommender Systems And The Next-Generation Web
Chapter 12 Recommendations In Ubiquitous Environments
Chapter 13 Summary And Outlook