Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
User profiles for personalized information access
The adaptive web
Information Systems Frontiers
Visualizing the Evolution of Users' Profiles from Online Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Detection of Overlapping Communities in Dynamical Social Networks
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Automated community detection on social networks: useful? efficient? asking the users
Proceedings of the 4th International Workshop on Web Intelligence & Communities
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nowadays, social networks are more and more widely used as a solution for enriching usersâ聙聶 profiles in systems such as recommender systems or personalized systems. For an unknown userâ聙聶s interest, the userâ聙聶s social network can be a meaningful data source for deriving that interest. However, in the literature very few techniques are designed to meet this solution. Existing techniques usually focus on people individually selected in the userâ聙聶s social network, and strongly depend on each authorâ聙聶s objective. To improve these techniques, we propose to use a community based algorithm that is applied to a part of the userâ聙聶s social network (egocentric network) and that can be reused for any purpose (e.g. personalization, recommendation). We compute weighted userâ聙聶s interests from these communities by considering their semantics (interests related to communities) and their structural measures (e.g. centrality measures) in the egocentric network graph. A first experiment conducted in Facebook demonstrates the usefulness of this technique compared to individuals based techniques, and the influence of structural measures (related to communities) on the quality of derived profiles. The results also raise the problem of usersâ聙聶 privacy in platforms such as online social networks. To enable users to better protect their privacy, these platforms should provide their users with a way to also make their friend list private.