  Recent social recommender systems benefit from friendship graph to make an
accurate recommendation, believing that friends in a social network have
exactly the same interests and preferences. Some studies have benefited from
hard clustering algorithms (such as K-means) to determine the similarity
between users and consequently to define degree of friendships. In this paper,
we went a step further to identify true friends for making even more realistic
recommendations. we calculated the similarity between users, as well as the
dependency between a user and an item. Our hypothesis is that due to the
uncertainties in user preferences, the fuzzy clustering, instead of the
classical hard clustering, is beneficial in accurate recommendations. We
incorporated the C-means algorithm to get different membership degrees of soft
users' clusters. Then, the users' similarity metric is defined according to the
soft clusters. Later, in a training scheme we determined the latent
representations of users and items, extracting from the huge and sparse
user-item-tag matrix using matrix factorization. In the parameter tuning, we
found the optimum coefficients for the influence of our soft social
regularization and the user-item dependency terms. Our experimental results
convinced that the proposed fuzzy similarity metric improves the
recommendations in real data compared to the baseline social recommender system
with the hard clustering.
