Apports des réseaux sociaux pour la gestion de la relation client

Depuis quelques annees, le Web s'est transforme en une plateforme d'echanges. La gestion de relation client doit evoluer pour tirer partie des donnees disponibles sur les reseaux sociaux et mettre l'entreprise au coeur des echanges. Nous proposons dans cet article une approche generique de detection de communautes de clients d'une entreprise, basee sur leur comportement explicite et implicite, integrant des donnees de sources diverses. Nous definissons une mesure de similarite, entre un utilisateur et un tag, prenant en compte la notation et la consultation des ressources et le reseau social de l'utilisateur. Nous validons cette approche sur une base exemple en utilisant deux methodes de detection de communautes pour trois cas d'utilisation.

[1]  Yiannis Kompatsiaris,et al.  Cluster-Based Landmark and Event Detection for Tagged Photo Collections , 2011, IEEE MultiMedia.

[2]  Georgia Koutrika,et al.  A Survey on Proximity Measures for Social Networks , 2012, SeCO Book.

[3]  Robert M. Haralick,et al.  Towards a Formal Concept Analysis Approach to Exploring Communities on the World Wide Web , 2005, ICFCA.

[4]  C. Perrin.  Dynamique identitaire et partitions sociales : le cas de l'identité "raciale" des noirs en france , 2011 .

[5]  Athena Vakali,et al.  Emotion aware clustering analysis as a tool for Web 2.0 communities detection: Implications for curriculum development , 2012 .

[6]  Meena Nagarajan,et al.  A CRM system for social media: challenges and experiences , 2013, WWW.

[7]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[8]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[10]  Huangmao Quan,et al.  Online Social Networks and Social Network Services: A Technical Survey , 2011 .

[11]  Ian H. Witten,et al.  Data Mining: Practical Machine Learning Tools and Techniques, 3/E , 2014 .

[12]  Jennifer Golbeck,et al.  Trust and nuanced profile similarity in online social networks , 2009, TWEB.

[13]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[14]  Eunmi Choi,et al.  Conceptual Modeling of Enterprise Application System Using Social Networking and Web 2.0 “Social CRM System” , 2008, 2008 International Conference on Convergence and Hybrid Information Technology.

[15]  Bin Wu,et al.  Group CRM: a new telecom CRM framework from social network perspective , 2009, CIKM-CNIKM.

[16]  Vittorio Loreto,et al.  Emergent Community Structure in Social Tagging Systems , 2008, Adv. Complex Syst..

[17]  Mason A. Porter,et al.  Communities in Networks , 2009, ArXiv.

[18]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[19]  Qi Gao,et al.  Analyzing Cross-System User Modeling on the Social Web , 2011, ICWE.

[20]  Ian Witten,et al.  Data Mining , 2000 .

[21]  Cordina Paul,et al.  Community management : fédérer des communautés sur les médias sociaux , 2013 .

[22]  Qi Gao,et al.  Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web , 2011, ESWC.

[23]  Wolfgang Nejdl,et al.  The Benefit of Using Tag-Based Profiles , 2007 .

[24]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Dayou Liu,et al.  Discovering Communities from Social Networks: Methodologies and Applications , 2010, Handbook of Social Network Technologies.

[26]  Jane Yung-jen Hsu,et al.  Tag-Based User Profiling for Social Media Recommendation , 2008 .

[27]  Michel Crampes,et al.  Survey on Social Community Detection , 2013, Social Media Retrieval.

[28]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Prem Melville.  Social Media Analytics: Channeling the Power of the Blogosphere for Marketing Insight , 2009 .

[30]  Gregory D. Abowd,et al.  A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications , 2001, Hum. Comput. Interact..

[31]  Hsinchun Chen,et al.  Identity matching using personal and social identity features , 2011, Inf. Syst. Frontiers.

[32]  Wolfgang Nejdl,et al.  The Benefit of Using Tag-Based Profiles , 2007, 2007 Latin American Web Conference (LA-WEB 2007).