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E-raamat: Recommender Systems [Wiley Online]

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  • Formaat: 252 pages
  • Sari: ISTE
  • Ilmumisaeg: 28-Nov-2014
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119054257
  • ISBN-13: 9781119054252
Teised raamatud teemal:
  • Wiley Online
  • Hind: 174,45 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Recommender SystemsSuurem pilt
  • Formaat: 252 pages
  • Sari: ISTE
  • Ilmumisaeg: 28-Nov-2014
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119054257
  • ISBN-13: 9781119054252
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119054252

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 Recommender Systems
1(24)
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 recommendation models
11(1)
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 e-commerce of content
16(2)
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 For Recommender Systems: The Case Of Social Media
25(28)
Jean-Claude Domenget
Alexandre Coutant
2.1 Introduction: the omnipresence of recommender systems
25(2)
2.2 The social approach to prescription
27(4)
2.2.1 The theory of the prescription and online interactions
27(2)
2.2.2 Conditions for recognition of the prescription
29(1)
2.2.3 The specificities of social media
30(1)
2.3 Users who do not focus on the prescriptions of platforms
31(14)
2.3.1 Facebook: the link, the type of activity and the context
32(6)
2.3.2 Twitter: prescription between peers and explanation of prescription
38(6)
2.3.3 Conditions for the recognition of a prescription: announcement and enunciation
44(1)
2.4 A guide for considering recommender systems adapted to different forms of social media
45(3)
2.5 Conclusion
48(1)
2.6 Bibliography
49(4)
Chapter 3 Recommender Systems And Social Networks: What Are The Implications For Digital Marketing?
53(18)
Maria Mercanti-Guerin
3.1 Social recommendations: an ancient practice revived by the digital age
54(4)
3.1.1 Recommendations: a difficult management for brands
55(1)
3.1.2 Internet recommendations: social presence and personalized recommendations
55(3)
3.2 Social recommendations: how are they used for e-commerce?
58(8)
3.2.1 Efficiency of recommender systems with regard to the performance of e-commerce websites
58(1)
3.2.2 Recommender systems used by social networks: from e-commerce to social commerce
59(7)
3.3 Conclusion
66(2)
3.4 Bibliography
68(3)
Chapter 4 Recommender Systems And Diversity: Taking Advantage Of The Long Tail And The Diversity Of Recommendation Lists
71(22)
Muriel Foulonneau
Valentin Groues
Yannick Naudet
Max Chevalier
4.1 The stakes associated with diversity within recommender systems
72(5)
4.1.1 Individual diversity or the individual perception of diversity
73(1)
4.1.2 The stakes and impacts of aggregate diversity
74(3)
4.2 Recommendation algorithms and diversity: trends, evaluation and optimization
77(8)
4.2.1 The tendency for recommendation algorithms to focus on the head
78(2)
4.2.2 The evaluation of diversity in recommender systems
80(1)
4.2.3 Recommendation algorithms which favor individual diversity
81(1)
4.2.4 Recommendation algorithms which favor aggregate diversity
81(1)
4.2.5 The shift toward user-centered diversity approaches
82(3)
4.3 Conclusion and new directions
85(2)
4.4 Bibliography
87(6)
Chapter 5 Isontre: Intelligent Transformer Of Social Networks Into A Recommendation Engine Environment
93(26)
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 history
97(4)
5.3.1 Collaborative filtering techniques
97(1)
5.3.2 General use social networks: what do they contain?
97(2)
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 networks
102(5)
5.4.2 iSoNTRE: the core of recommendation
107(3)
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 Approach For Document Search
119(16)
Manel Hmimida
Rushed Kanawati
6.1 Introduction
119(1)
6.2 Tag recommendation: a brief state of the art
120(2)
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 Recommendation To Enable An Interactive Guidance Of Product Line Configuration
135(22)
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 interactive PL configuration
141(1)
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 Frontier Of Recommender Systems
157(34)
Hakim Hachour
Samuel Szoniecky
Safia Abouad
8.1 Introduction
157(2)
8.2 Latest developments: finalized activities, recommender systems and the relevance of information
159(10)
8.2.1 Cognitive dynamics of finalized activities
159(2)
8.2.2 The foundations of recommender systems
161(5)
8.2.3 What information relevance?
166(3)
8.3 Observable interests for decision theory: a combination of content-based, collaboration-based and knowledge-based recommendations
169(8)
8.3.1 Methodology: meta-analysis and modeling of the process
169(2)
8.3.2 Analysis and modeling of a macro-process for responding to a call for R&D projects
171(2)
8.3.3 Analysis and model of a socio-organizational tool for the management of customer complaints
173(4)
8.4 Discussion and conclusions
177(4)
8.4.1 Discussion: the performance of the filtering methods and semio-cognitive criteria for relevance
177(4)
8.5 Conclusions: recommender systems linked to finalized activities
181(4)
8.5.1 The localization of activities and geographical information systems: a new kind of data
182(1)
8.5.2 Transparency of the use of personal data, data protection and ownership
183(2)
8.6 Acknowledgments
185(1)
8.7 Bibliography
185(6)
Chapter 9 The French-Speaking Literary Prescription Market In Networks
191(22)
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 prescription market
194(2)
9.3 Methodology
196(1)
9.4 The competitive structure of the market of online social networks of readers
197(7)
9.4.1 Pure player networks and the audience strategy
199(2)
9.4.2 Amateur networks and the survival strategy
201(1)
9.4.3 Backed networks and the hybridization strategy
202(2)
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 literary prescription?
208(2)
9.7 Appendix: list of interviews undertaken
210(1)
9.8 Bibliography
210(3)
Chapter 10 Presentation Of Offered Services: Babelio, A Recommendation Engine Dedicated To Books
213(8)
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 pertinence
217(1)
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 Services: Nomao, Recommender Systems And Information Search
221(6)
Estelle Delpech
Laurent Candillier
Etienne Chai
11.1 Introduction: the actors of Internet recommendation
221(1)
11.2 Approaches to recommendation
222(1)
11.3 Nomao: a local outlets search and recommendation engine
223(2)
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 interactive recommender systems
225(1)
11.5 Appendix
226(1)
List Of Authors 227(4)
Index 231
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.
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