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Recommender Systems are an integral part of music sharing platforms. Oftenthe aim of these systems is to increase the time, the user spends on theplatform and hence having a high commercial value. The systems which aim atincreasing the average time a user spends on the platform often need torecommend songs which the user might want to listen to next at each point intime. This is different from recommendation systems which try to predict theitem which might be of interest to the user at some point in the user lifetimebut not necessarily in the very near future. Prediction of the next song theuser might like requires some kind of modeling of the user interests at thegiven point of time. Attentive neural networks have been exploiting thesequence in which the items were selected by the user to model the implicitshort-term interests of the user for the task of next item prediction, howeverwe feel that the features of the songs occurring in the sequence could alsoconvey some important information about the short-term user interest which onlythe items cannot. In this direction, we propose a novel attentive neuralarchitecture which in addition to the sequence of items selected by the user,uses the features of these items to better learn the user short-termpreferences and recommend the next song to the user.

Attentive Neural Architecture Incorporating Song Features For Music Recommendation