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Recommender systems aim to identify relevant items for particular users in large-scale online applications. The historical rating data of users is a valuable input resource for many recommendation models such as collaborative filtering (CF), but these models are known to suffer from the rating sparsity problem when the users or items under consideration have insufficient rating records. With the continued growth of online social networks, the increased user-to-user relationships are reported todoi:10.1109/tcyb.2016.2595620 pmid:28113690 fatcat:qt6naqquwrbixoaqoi5gsegu6m