A Community Based Algorithm for Deriving Users' Profiles from Egocentrics Networks

Nowadays, social networks are more and more widely used as a solution for enriching users' profiles in systems such as recommender systems or personalized systems. For an unknown user's interest, the user'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 user's social network, and strongly depend on each author's objective. To improve these techniques, we propose to use a community based algorithm that is applied to a part of the user's social network (egocentric network) and that can be reused for any purpose (e.g. personalization, recommendation). We compute weighted user'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 users' 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 friendlist private.