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Attentive Neural Architecture Incorporating Song Features For Music Recommendation
Recommender Systems are an integral part of music sharing platforms. Often
the aim of these systems is to increase the time, the user spends on the
platform and hence having a high commercial value. The systems which aim at
increasing the average time a user spends on the platform often need to
recommend songs which the user might want to listen to next at each point in
time. This is different from recommendation systems which try to predict the
item which might be of interest to the user at some point in the user lifetime
but not necessarily in the very near future. Prediction of the next song the
user might like requires some kind of modeling of the user interests at the
given point of time. Attentive neural networks have been exploiting the
sequence in which the items were selected by the user to model the implicit
short-term interests of the user for the task of next item prediction, however
we feel that the features of the songs occurring in the sequence could also
convey some important information about the short-term user interest which only
the items cannot. In this direction, we propose a novel attentive neural
architecture which in addition to the sequence of items selected by the user,
uses the features of these items to better learn the user short-term
preferences and recommend the next song to the user.Comment: Accepted as a paper at the 12th ACM Conference on Recommender Systems
(RecSys 18
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