Our work also incorporates the introduction of a heuristic algorithm that assigns new users that join the service into appropriate social groups, once the service has been initialized and the groups have been assessed using spectral clustering. Regarding profile’s cardinality impact on the system performance, this is shown to be highly dependent on the underlying distribution that characterizes the frequency of user preferences appearance. The experimental results indicate that spectral clustering, due to the optimization it offers in terms of normalized cut minimization, is applicable within the context of Magnet Beyond socialization services. on the basis of equal-sized group formation and of maximization of interests’ commonalities between users within each social group). Such an evaluation study is performed in the context of our service requirements (i.e. This allows one to run an evaluation study of three widely used clustering methods (k-means, hierarchical and spectral clustering) in the scope of social groups assessment and in regard to the cardinality of the profile used to assess users’ preferences. We introduce an integrated social networking framework through the definition or the appropriate notions and metrics. In this paper, we provide the results of ongoing work in Magnet Beyond project, regarding social networking services.
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