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In recent years, streaming music platforms have become very popular mainlydue to the huge number of songs these systems make available to users. Thisenormous availability means that recommendation mechanisms that help users toselect the music they like need to be incorporated. However, developingreliable recommender systems in the music field involves dealing with manyproblems, some of which are generic and widely studied in the literature, whileothers are specific to this application domain and are therefore lesswell-known. This work is focused on two important issues that have not receivedmuch attention: managing gray-sheep users and obtaining implicit ratings. Thefirst one is usually addressed by resorting to content information that isoften difficult to obtain. The other drawback is related to the sparsityproblem that arises when there are obstacles to gather explicit ratings. Inthis work, the referred shortcomings are addressed by means of a recommendationapproach based on the users' streaming sessions. The method is aimed atmanaging the well-known power-law probability distribution representing thelistening behavior of users. This proposal improves the recommendationreliability of collaborative filtering methods while reducing the complexity ofthe procedures used so far to deal with the gray-sheep problem.

A session-based song recommendation approach involving user characterization along the play power-law distribution