Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Effects of maximum flow algorithm on identifying web community
Proceedings of the 4th international workshop on Web information and data management
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The Benefit of Using Tag-Based Profiles
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
Trust and nuanced profile similarity in online social networks
ACM Transactions on the Web (TWEB)
Group CRM: a new telecom CRM framework from social network perspective
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Semantic enrichment of twitter posts for user profile construction on the social web
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Analyzing cross-system user modeling on the social web
ICWE'11 Proceedings of the 11th international conference on Web engineering
A survey on proximity measures for social networks
Search Computing
A CRM system for social media: challenges and experiences
Proceedings of the 22nd international conference on World Wide Web
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In recent years, the Web has evolved into an exchange platform. Customer Relationship Management (CRM) must follow this evolution and connect CRM tools to social networks in order to place companies in the center of all the exchanges. We propose, in this article, a community detection approach that identifies clusters of customers of a company using their explicit and implicit behaviour. Our contribution is the definition of a composite profile that integrates various informations gathered from different applications, such as the information system of the company, the existing CRM, or Twitter. We define a similarity measure, between a user and a tag, that takes into account the rating and consultation of resources, as well as actions on social networks and user contacts. We validate this approach against a test database and we discuss results and future works.