Abstract
Driving and music listening are two inseparable everyday activities for millions of people today in the world. Considering the high correlation between music, mood and driving comfort and safety, it makes sense to use appropriate and intelligent music recommendations based on the mood of drivers and songs in the context of car driving. The objective of this paper is to present the project of a contextual mood-based music recommender system capable of regulating the driver’s mood and trying to have a positive influence on her driving behaviour. Here we present the proof of concept of the system and describe the techniques and technologies that are part of it. Further possible future improvements on each of the building blocks are also presented.
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This work was done at the Joint Open Lab MobiLAB and was supported by a fellowship from TIM.
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Çano, E., Coppola, R., Gargiulo, E., Marengo, M., Morisio, M. (2017). Mood-Based On-Car Music Recommendations. In: Maglaras, L., Janicke, H., Jones, K. (eds) Industrial Networks and Intelligent Systems. INISCOM 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 188. Springer, Cham. https://doi.org/10.1007/978-3-319-52569-3_14
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