User - Profile based Communities Assessment using Clustering Methods

In this paper, we introduce and evaluate a framework for a user profile-based socializing application. We model the user profile as an unordered set composed of n keywords that represent users' preferences. We evaluate the effectiveness of two clustering algorithms, k-means and spectral clustering in the scope of social groups' assessment. Through experimental results we substantiate the applicability of spectral clustering in the examined service and we evaluate the impact of profile size in terms of the quality of partitions yielded by spectral clustering. The above are performed using case studies and scenarios developed in the context of 1ST project's MAGNET Beyond pilot services definition.