Abstract
Music streaming services have opened the possibility of accessing huge quantities of songs and more sophisticated data can be utilized by recommender systems to improve their performance. Some recommendation methods dealing with different music features have been proposed during the last years, but most of them do not consider emotional aspects. The recommender system presented in this work, allows the classification of the music into emotions from acoustic characteristics extracted directly by means of an automatic analysis of the songs. These emotional aspects of the songs are incorporated to the proposed recommendation models, that also include recommendations to groups of users from their social relationships. The experiments show an improvement in the recommendation reliability obtained by this proposal against the classic collaborative filtering recommendation approaches, for both individual and group recommendations.
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Acknowledgements
This research has been supported by the Department of Education of the Junta de Castilla y León, Spain, (ORDEN EDU/667/2019). Project code: SA064G19.
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Gomes, C.J., Gil-González, A.B., Luis-Reboredo, A., Sánchez-Moreno, D., Moreno-García, M.N. (2022). Song Recommender System Based on Emotional Aspects and Social Relations. In: Matsui, K., Omatu, S., Yigitcanlar, T., González, S.R. (eds) Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference. DCAI 2021. Lecture Notes in Networks and Systems, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-86261-9_9
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