Abstract
The streaming services are here to stay. In recent years we have witnessed their consolidation and success, which is manifested in their exponential growth, while the sale of songs/albums in physical or digital format has declined. An important part of these services are recommendation systems, which facilitate the exploration of content to users. This article proposes a content-based approach, using the One-Class Support Vector Machine classification algorithm as an anomaly detector. The aim is to generate a playlist that adapts to the user’s tastes, incorporating the novelties of new releases. The model is capable of detecting elements that belong to the profile of the user’s tastes with great accuracy, facilitating the implementation of an Android mobile application that scans and detects changes in user preferences. This will make it possible not only to manage the playlist that has been recommended, but also periodically to incorporate new songs to the profile from the list of new music.
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Acknowledgments
This paper has been partially supported by: La convocatoria en concurrencia no competitiva para la concesión de subvenciones para la realización de proyectos de I+D de las PYMES de la Agencia de Innovación, Financiación e Internacionalización Empresarial de Castilla y León (ADE). Research Project: Mejora de habilidades operativas ante riesgos emergentes en entornos inteligentes de producción: aplicación a las redes eléctricas inteligentes.
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Yepes, F.A., López, V.F., Pérez-Marcos, J., Gil, A.B., Villarrubia, G. (2018). Listen to This: Music Recommendation Based on One-Class Support Vector Machine. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_39
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