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Online streaming services have become the most popular way of listening tomusic. The majority of these services are endowed with recommendationmechanisms that help users to discover songs and artists that may interest themfrom the vast amount of music available. However, many are not reliable as theymay not take into account contextual aspects or the ever-evolving userbehavior. Therefore, it is necessary to develop systems that consider theseaspects. In the field of music, time is one of the most important factorsinfluencing user preferences and managing its effects, and is the motivationbehind the work presented in this paper. Here, the temporal informationregarding when songs are played is examined. The purpose is to model both theevolution of user preferences in the form of evolving implicit ratings and userlistening behavior. In the collaborative filtering method proposed in thiswork, daily listening habits are captured in order to characterize users andprovide them with more reliable recommendations. The results of the validationprove that this approach outperforms other methods in generating bothcontext-aware and context-free recommendations

applied sciences

Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit  User Preferences and User Listening Habits in A Collaborative Filtering  Approach