In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make\navailable to users. This enormous availability means that recommendation mechanisms that help users to select the music they like\nneed to be incorporated. However, developing reliable recommender systems in the music field involves dealing with many\nproblems, some of which are generic and widely studied in the literature while others are specific to this application domain and\nare therefore less well-known. This work is focused on two important issues that have not received much attention: managing\ngray-sheep users and obtaining implicit ratings. The first one is usually addressed by resorting to content information that is often\ndifficult to obtain. The other drawback is related to the sparsity problem that arises when there are obstacles to gather explicit\nratings. In this work, the referred shortcomings are addressed by means of a recommendation approach based on the usersâ??\nstreaming sessions. The method is aimed at managing the well-known power-law probability distribution representing the\nlistening behavior of users. This proposal improves the recommendation reliability of collaborative filtering methods while\nreducing the complexity of the procedures used so far to deal with the gray-sheep problem.
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