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.

Autorentext

Lecturer at the GERiiCO laboratory at University Lille 3, Gerald Kembellec specializes in information science and communication.

Professor of Documentary Engineering Chair of CNAM, Ghislaine Chartron is director of the National Institute of Science and Technical documentation.

Professor at the University Paris 8, Imad Saleh is the Paragraph laboratory director and director of the graduate school Cognition Language Interaction.

Inhalt

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
Jean-Claude DOMENGET and Alexandre COUTANT

2.1. Introduction: the omnipresence of recommender systems 25

2.2. The social approach to prescription 27

2.2.1. The theory of the prescription and online interactions 27

2.2.2. Conditions for recognition of the prescription 29

2.2.3. The specificities of social media 30

2.3. Users who do not focus on the prescriptions of platforms 31

2.3.1. Facebook: the link, the type of activity and the context 32

2.3.2. Twitter: prescription between peers and explanation of prescription 38

2.3.3. Conditions for the recognition of a prescription: announcement and enunciation 44

2.4. A guide for considering recommender systems adapted to different forms of social media 45

2.5. Conclusion 48

2.6. Bibliography 49

CHAPTER 3. RECOMMENDER SYSTEMS AND SOCIAL NETWORKS: WHAT ARE THE IMPLICATIONS FOR DIGITAL MARKETING? 53
Maria MERCANTI-GUÉRIN

3.1. Social recommendations: an ancient practice revived by the digital age 54

3.1.1. Recommendations: a difficult management for brands 55

3.1.2. Internet recommendations: social presence and personalized recommendations 55

3.2. Social recommendations: how are they used for e-commerce? 58

3.2.1. Efficiency of recommender systems with regard to the performance of e-commerce websites 58

3.2.2. Recommender systems used by social networks: from e-commerce to social commerce 59

3.3. Conclusion 66

3.4. Bibliography 68

CHAPTER 4. RECOMMENDER SYSTEMS AND DIVERSITY: TAKING ADVANTAGE OF THE LONG TAIL AND THE DIVERSITY OF RECOMMENDATION LISTS 71
Muriel FOULONNEAU, Valentin GROUÈS, Yannick NAUDET and Max CHEVALIER

4.1. The stakes associated with diversity within recommender systems 72

4.1.1. Individual diversity or the individual perception of diversity 73

4.1.2. The stakes and impacts of aggregate diversity 74

4.2. Recommendation algorithms and diversity: trends, evaluation and optimization 77

4.2.1. The tendency for recommendation algorithms to focus on the head 78

4.2.2. The evaluation of diversity in recommender systems 80

4.2.3. Recommendation algorithms which favor individual diversity 81

4.2.4. Recommendation algorithms which favor aggregate diversity 81

4.2.5. The shift toward user-centered diversity approaches 82

4.3. Conclusion and new directions 85

4.4. Bibliography 87

CHAPTER 5. ISONTRE: INTELLIGENT TRANSFORMER OF SOCIAL NETWORKS INTO A RECOMMENDATION ENGINE ENVIRONMENT 93
Rana CHAMSI ABU QUBA, Salima HASSAS, Usama FAYYAD, Hammam CHAMSI and Christine GERTOSIO

5.1. Summary 93

5.2. Introduction 94

5.3. Latest developments, definition and history 97

5.3.1. Collaborative filtering techniques 97

5.3.2. General use social networks: what do they contain? 97

5.3.3. Social recommendation 99

5.3.4. The recommendation of concepts 100

5.4. iSoNTRE 101

5.4.1. iSoNTRE: transformer of social networks 102 5.4.2. iSoNTRE: the core of recommendation 107</p&g...