## 1 Introduction  
The arrival of streaming services has changed the way users listen to music. Currently, music listeners do not need to store the songs they want to listen to but can access and play an almost unlimited number of songs online. This fact has given rise to the need for endowing these systems with personalized recommendation mechanisms that make it easier for users to find songs that suit their tastes. Most streaming music platforms have increasingly advanced personalization tools to offer music to users and even to show them ads. This is a key aspect of the platform's positioning against their competitors and increases their revenues. To get an idea of the economic power of these platforms, Spotify, one of the most popular platforms, reached a revenue of 2 billion USD in the first quarter of 2020, 161 million USD of which was due to ads [1]. For several years, research on recommender systems has been very intense, and more recently the focus has been centered on context-aware systems, which aim to adapt recommendations according to the circumstances surrounding the user. To do so, different contextual factors, such as the location, time, weather, companions, and emotions, among others, are added as additional dimensions to the recommendation models. Moreover, time is an essential factor since it not only has an effect on the user context, but also influences the drift of user preferences. In the music domain, dealing with the effect of time on recommendations is different from other fields, especially in the context of streaming services. Therefore, approaches used in some domains may be difficult to be reused or extended to this area. This is mainly due to the differences in the way music is consumed compared to other products. A music item is usually played many times by the same user, while a book is generally read only once. Another difference is the number of items that can be consumed in a given period, which can be high for music and lower for other items such as books. Likewise, the evolution of preferences may be different depending on the application domain since in some domains, interests evolve quickly, while in others these changes occur more slowly. Music, on the other hand, is found somewhere in the middle, where changes occur at a more even pace. In addition, when considering time as a contextual variable, its effects on the field of music are quite different from those generated in other areas. For instance, the time of day can have little influence when choosing a book but can have a significant influence when choosing a song. The previously described issues may give rise to additional problems when explicit ratings are not available, and it becomes necessary to resort to implicit feedback. The common methods for obtaining implicit ratings, such as using the number of records purchased or the time spent examining items, are not suitable for the music domain. The reason for this is that music items are not individually purchased on streaming platforms, and that the same item can be listened to many times by the same user. This forces music preference evolution, managed from the perspective of implicit feedback, to be different from other areas. Quite often, the number of times a song is played is used to derive implicit ratings about a particular musical item. However, computing ratings over time, as a means to manage the drift in preference, requires establishing time windows to distribute timestamped plays and compute the evolving ratings for each interval. In other domains, the procedure is more straightforward, since only an observation per user and item is needed to apply a decay function or to discard old ratings. The observation can correspond to an action, such as a purchase. Nevertheless, timestamped ratings of music items require several observations in the form of plays for each time window. The same problem arises in context-aware recommender systems when setting time contexts.