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
Gerald Kembellec
Ghislaine Chartron
Imad Saleh
Chapter 1 General Introduction To 1 (24)
Recommender Systems
Ghislaine Chartron
Gerald Kembellec
1.1 Putting it into perspective 1 (1)
1.2 An interdisciplinary subject 2 (2)
1.3 The fundamentals of algorithms 4 (7)
1.3.1 Collaborative filtering 4 (3)
1.3.2 Content filtering 7 (2)
1.3.3 Hybrid methods 9 (2)
1.3.4 Conclusion on historical 11 (1)
recommendation models
1.4 Content offers and recommender systems 11 (8)
1.4.1 Culture and recommender systems 11 (5)
1.4.2 Recommender systems and the 16 (2)
e-commerce of content
1.4.3 The behavior of users 18 (1)
1.5 Current issues 19 (1)
1.6 Bibliography 19 (6)
Chapter 2 Understanding Users' Expectations 25 (28)
For Recommender Systems: The Case Of Social
Media
Jean-Claude Domenget
Alexandre Coutant
2.1 Introduction: the omnipresence of 25 (2)
recommender systems
2.2 The social approach to prescription 27 (4)
2.2.1 The theory of the prescription 27 (2)
and online interactions
2.2.2 Conditions for recognition of the 29 (1)
prescription
2.2.3 The specificities of social media 30 (1)
2.3 Users who do not focus on the 31 (14)
prescriptions of platforms
2.3.1 Facebook: the link, the type of 32 (6)
activity and the context
2.3.2 Twitter: prescription between 38 (6)
peers and explanation of prescription
2.3.3 Conditions for the recognition of 44 (1)
a prescription: announcement and
enunciation
2.4 A guide for considering recommender 45 (3)
systems adapted to different forms of
social media
2.5 Conclusion 48 (1)
2.6 Bibliography 49 (4)
Chapter 3 Recommender Systems And Social 53 (18)
Networks: What Are The Implications For
Digital Marketing?
Maria Mercanti-Guerin
3.1 Social recommendations: an ancient 54 (4)
practice revived by the digital age
3.1.1 Recommendations: a difficult 55 (1)
management for brands
3.1.2 Internet recommendations: social 55 (3)
presence and personalized
recommendations
3.2 Social recommendations: how are they 58 (8)
used for e-commerce?
3.2.1 Efficiency of recommender systems 58 (1)
with regard to the performance of
e-commerce websites
3.2.2 Recommender systems used by 59 (7)
social networks: from e-commerce to
social commerce
3.3 Conclusion 66 (2)
3.4 Bibliography 68 (3)
Chapter 4 Recommender Systems And 71 (22)
Diversity: Taking Advantage Of The Long
Tail And The Diversity Of Recommendation
Lists
Muriel Foulonneau
Valentin Groues
Yannick Naudet
Max Chevalier
4.1 The stakes associated with diversity 72 (5)
within recommender systems
4.1.1 Individual diversity or the 73 (1)
individual perception of diversity
4.1.2 The stakes and impacts of 74 (3)
aggregate diversity
4.2 Recommendation algorithms and 77 (8)
diversity: trends, evaluation and
optimization
4.2.1 The tendency for recommendation 78 (2)
algorithms to focus on the head
4.2.2 The evaluation of diversity in 80 (1)
recommender systems
4.2.3 Recommendation algorithms which 81 (1)
favor individual diversity
4.2.4 Recommendation algorithms which 81 (1)
favor aggregate diversity
4.2.5 The shift toward user-centered 82 (3)
diversity approaches
4.3 Conclusion and new directions 85 (2)
4.4 Bibliography 87 (6)
Chapter 5 Isontre: Intelligent Transformer 93 (26)
Of Social Networks Into A Recommendation
Engine Environment
Rana Chamsi Abu Quba
Salima Hassas
Usama Fayyad
Hammam Chamsi
Christine Gertosio
5.1 Summary 93 (1)
5.2 Introduction 94 (3)
5.3 Latest developments, definition and 97 (4)
history
5.3.1 Collaborative filtering techniques 97 (1)
5.3.2 General use social networks: what 97 (2)
do they contain?
5.3.3 Social recommendation 99 (1)
5.3.4 The recommendation of concepts 100(1)
5.4 iSoNTRE 101(9)
5.4.1 iSoNTRE: transformer of social 102(5)
networks
5.4.2 iSoNTRE: the core of 107(3)
recommendation
5.5 Experiments 110(4)
5.5.1 The preparation of data 110(1)
5.5.2 Testing methodology 110(1)
5.5.3 The creation of avatars 111(1)
5.5.4 Results 112(1)
5.5.5 Discussion 113(1)
5.6 Conclusion 114(1)
5.7 Bibliography 115(4)
Chapter 6 A Two-Level Recommendation 119(16)
Approach For Document Search
Manel Hmimida
Rushed Kanawati
6.1 Introduction 119(1)
6.2 Tag recommendation: a brief state of 120(2)
the art
6.3 The hypertagging system 122(2)
6.3.1 Metadata 122(1)
6.3.2 Architecture 123(1)
6.4 Recommendation approach 124(3)
6.4.1 Presentation 124(2)
6.4.2 Recommendation algorithm 126(1)
6.5 Evaluation 127(4)
6.5.1 Generation of facets 127(2)
6.5.2 Generation of association rules 129(1)
6.5.3 Evaluation of recommendation rules 130(1)
6.6 Conclusion 131(1)
6.7 Bibliography 132(3)
Chapter 7 Combining Configuration And 135(22)
Recommendation To Enable An Interactive
Guidance Of Product Line Configuration
Raouia Triki
Raul Mazo
Camille Salinesi
7.1 Introduction 135(2)
7.2 Context 137(5)
7.2.1 Configuration 137(2)
7.2.2 Recommendation 139(2)
7.2.3 Obstacles and challenges of 141(1)
interactive PL configuration
7.3 Overview of the proposed approach 142(6)
7.4 Preliminary evaluation 148(1)
7.5 Discussion and related work 148(3)
7.5.1 Recommendation techniques 148(3)
7.6 Conclusion and future work 151(1)
7.7 Bibliography 151(6)
Chapter 8 Semio-Cognitive Spaces: The 157(34)
Frontier Of Recommender Systems
Hakim Hachour
Samuel Szoniecky
Safia Abouad
8.1 Introduction 157(2)
8.2 Latest developments: finalized 159(10)
activities, recommender systems and the
relevance of information
8.2.1 Cognitive dynamics of finalized 159(2)
activities
8.2.2 The foundations of recommender 161(5)
systems
8.2.3 What information relevance? 166(3)
8.3 Observable interests for decision 169(8)
theory: a combination of content-based,
collaboration-based and knowledge-based
recommendations
8.3.1 Methodology: meta-analysis and 169(2)
modeling of the process
8.3.2 Analysis and modeling of a 171(2)
macro-process for responding to a call
for R&D projects
8.3.3 Analysis and model of a 173(4)
socio-organizational tool for the
management of customer complaints
8.4 Discussion and conclusions 177(4)
8.4.1 Discussion: the performance of 177(4)
the filtering methods and
semio-cognitive criteria for relevance
8.5 Conclusions: recommender systems 181(4)
linked to finalized activities
8.5.1 The localization of activities 182(1)
and geographical information systems: a
new kind of data
8.5.2 Transparency of the use of 183(2)
personal data, data protection and
ownership
8.6 Acknowledgments 185(1)
8.7 Bibliography 185(6)
Chapter 9 The French-Speaking Literary 191(22)
Prescription Market In Networks
Louis Wiart
9.1 Introduction 191(2)
9.2 The economy of prescription 193(3)
9.2.1 The notion of prescription 193(1)
9.2.2 From the advisors market to the 194(2)
prescription market
9.3 Methodology 196(1)
9.4 The competitive structure of the 197(7)
market of online social networks of
readers
9.4.1 Pure player networks and the 199(2)
audience strategy
9.4.2 Amateur networks and the survival 201(1)
strategy
9.4.3 Backed networks and the 202(2)
hybridization strategy
9.5 The organization of prescription 204(4)
9.5.1 Social prescription 205(1)
9.5.2 Editorial prescription 206(1)
9.5.3 Algorithmic prescription 207(1)
9.6 Conclusion: what legitimacy for 208(2)
literary prescription?
9.7 Appendix: list of interviews 210(1)
undertaken
9.8 Bibliography 210(3)
Chapter 10 Presentation Of Offered 213(8)
Services: Babelio, A Recommendation Engine
Dedicated To Books
Vassil Stefanov
Guillaume Teisseire
Pierre Fremaux
10.1 Introduction 213(3)
10.2 The problem of qualitative pertinence 216(1)
10.3 The problem of quantitative 217(1)
pertinence
10.4 Balancing recall and precision 217(1)
10.5 The issue of sparse data 218(1)
10.6 Performance and scalability 218(1)
10.7 A few issues specific to books 219(2)
Chapter 11 Presentation Of The Offer Of 221(6)
Services: Nomao, Recommender Systems And
Information Search
Estelle Delpech
Laurent Candillier
Etienne Chai
11.1 Introduction: the actors of Internet 221(1)
recommendation
11.2 Approaches to recommendation 222(1)
11.3 Nomao: a local outlets search and 223(2)
recommendation engine
11.3.1 Popularity score 223(1)
11.3.2 Affinity score 224(1)
11.3.3 Social recommendation 225(1)
11.4 Prospects: the move toward 225(1)
interactive recommender systems
11.5 Appendix 226(1)
List Of Authors 227(4)
Index 231
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