Online Social Networks (OSNs) have found widespread applications in every area of our\nlife. A large number of people have signed up to OSN for different purposes, including to meet\nold friends, to choose a given company, to identify expert users about a given topic, producing\na large number of social connections. These aspects have led to the birth of a new generation\nof OSNs, called Multimedia Social Networks (MSNs), in which user-generated content plays a key\nrole to enable interactions among users. In this work, we propose a novel expert-finding technique\nexploiting a hypergraph-based data model for MSNs. In particular, some user-ranking measures,\nobtained considering only particular useful hyperpaths, have been profitably used to evaluate the\nrelated expertness degree with respect to a given social topic. Several experiments on Last.FM have\nbeen performed to evaluate the proposed approachâ??s effectiveness, encouraging future work in this\ndirection for supporting several applications such as multimedia recommendation, influence analysis,\nand so on.
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