Attentive Neural Architecture Incorporating Song Features For Music Recommendation

Abstract

Recommender Systems are an integral part of music sharing plat-forms. Often the aim of these systems is to increase the time, theuser spends on the platform and hence having a high commercialvalue. The systems which aim at increasing the average time auser spends on the platform often need to recommend songs whichthe user might want to listen to next at each point in time. This isdifferent from recommendation systems which try to predict theitem which might be of interest to the user at some point in theuser lifetime but not necessarily in the very near future. Predictionof next song the user might like requires some kind of modelingof the user interests at the given point of time. Attentive neuralnetworks have been exploiting the sequence in which the itemswere selected by the user to model the implicit short-term interestsof the user for the task of next item prediction, however we feelthat features of the songs occurring in the sequence could also con-vey some important information about the short-term user interestwhich only the items cannot. In this direction we propose a novelattentive neural architecture which in addition to the sequence ofitems selected by the user, uses the features of these items to betterlearn the user short-term preferences and recommend next song tothe user.

Publication
In the 12th ACM International Conference on Recommender Systems (RecSys)