Recommender Systems [Hardcover]

Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales.

On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.

PREFACE xi
Gérald KEMBELLEC, Ghislaine CHARTRON and Imad SALEH

CHAPTER 1. GENERAL INTRODUCTION TO RECOMMENDER SYSTEMS 1
Ghislaine CHARTRON and Gérald KEMBELLEC

1.1. Putting it into perspective 1

1.2. An interdisciplinary subject 2

1.3. The fundamentals of algorithms 4

1.3.1. Collaborative filtering 4

1.3.2. Content filtering 7

1.3.3. Hybrid methods 9

1.3.4. Conclusion on historical recommendation models 11

1.4. Content offers and recommender systems 11

1.4.1. Culture and recommender systems 11

1.4.2. Recommender systems and the e-commerce of content 16

1.4.3. The behavior of users 18

1.5. Current issues 19

1.6. Bibliography 19

CHAPTER 2. UNDERSTANDING USERS’ EXPECTATIONS FOR RECOMMENDER SYSTEMS: THE CASE OF SOCIAL MEDIA 25