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