Recommender system improvement cases through implicit feedbacks from social network

Recommender systems (RS) performance largely depends on diverse types of input that characterize users' preference in the form of both explicit and implicit feedbacks. An explicit feedback is stated directly by an explicit input from users regarding their interest in some options of services or products. Such feedback, however, is not always available. On the other hand, an implicit feedback, which reflects users' opinion indirectly through user behavior is far more abundant. In this paper, we elaborate several ways to improve the RS of three real cases dataset (online travel service, online transportation, and telecommunication service provider) through implicit feedbacks. In the first case, we analyze the effect of a simple feedback from users' input during registration without using any social network analysis (SNA). In the second case, we analyze the effect of community structure extracted from its SNA as its additional attributes. In the third case, we analyze the effect of more additional feedback attributes (modularity, PageRank, eigenvector centrality, clustering coefficient, weighted in degree, weighted outdegree, weighted degree) which also obtained from the SNA of the corresponding dataset. Given the right hyperparameter settings, we observed RS improvement in term of RMSE (root mean square error) in the three cases. In this paper, three RS models: SVD, SVD++, and difference SVD are used. Besides discussing the RS performance, we also discuss the computational cost incurred from incorporating those implicit feedbacks.