Semantic link-based Model for User Recommendation in Online community

Recommendation systems have been widely used to overcome the problem of information overloading and help people to make the right decision of needed items, such as: movies, books, products, or even people. This paper proposes a semantic model for people recommendation in online community. This model predicts significant links that would be established between community' members even they do not know each other using cascaded collaborative filtering (CF). Two major steps in this type of recommendation model are (i) the method used to compute the similarity between people, and (ii) the method used to combine these similarities in order to compute the overall similarity between target member and others. By utilizing local features of links and nodes, similarity measurements between members are calculated. Semantic relatedness between members is delivered from connection strength and trust score in order to identify the closeness between them. Extensive experimental of the proposed model using real dataset of scientific community was applied to recommend authors as possible coworkers for a target researcher. Experimental results on real dataset from publication network show that the proposed model for people recommendation outperforms other known techniques in ranking recommended collaborators.