Dynamic Clustering Process to Calculate Affinity Degree of Users as Basis of a Social Network Recommender

Abstract

Social networking has become a reality: links, activities, and recommendations are proposed by networked friends every moment. There is a need to filter such information to make user enjoy such an experience. In the same way recommender systems where proposed to ease the browsing experience of navigators, nowadays recommenders are required to help users in sharing and obtaining the appropriate information on the social networks. The challenges behind are not only related to the continuous evolution of information being shared, but also by the fact that ubiquity is today a reality. Consequently recommender should take into account the context of the user to whom recommendations are being done. In this paper we present a recommender for social networks that is able to update recommendations as information evolves. The recommender is based on a graph build on basis of a data mining component that extract knowledge on relations and information exchanged by users. The mining component can run autonomously so recommendations can be updated if required. The paper also presents preliminary analysis on the performance of the proposed recommender.