Abstract
Day by day, online content delivery services suppliers grow the volume of data on the internet. Music streaming services are one of those services that increase the number of users every day, as well as the number of songs in their catalog. To help the users to find songs that fit their interests, music recommender systems can be used to filter a large number of songs according to the preference of the user. However, the context in which the users listen to songs must be taken into account, which justifies the usage of context-aware recommender systems. The goal of this work is to use a Dual Recurrent Neural Network to acquire contextual information (represented by embeddings) for each song, given the sequence of songs that each user has listened to. We evaluated the embeddings by using four context-aware music recommender systems in two datasets. The results showed that the embeddings (i.e. the contextual information) obtained by our proposed method are able to improve context-aware music recommender systems.
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Acknowledgments
To CNPq/Brazil (grant #403648/2016-5) for financial support and NVIDIA Corporation for donation of a Titan V GPU used in this work. This work was also supported by grant 2019/25010-5, Sao Paulo Research Foundation (FAPESP).
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Santana, I.A.P., Domingues, M.A. (2021). Improving Context-Aware Music Recommender Systems with a Dual Recurrent Neural Network. In: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_22
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