Abstract
In an automatic music playlist generator, such as an automated online radio channel, how should the system react when a user hits the skip button? Can we use this type of negative feedback to improve the list of songs we will playback for the user next? We propose SkipAwareRec, a next-item recommendation system based on reinforcement learning. SkipAwareRec recommends the best next music categories, considering positive feedback consisting of normal listening behaviour, and negative feedback in the form of song skips. Since SkipAwareRec recommends broad categories, it needs to be coupled with a model able to choose the best individual items. To do this, we propose Hybrid SkipAwareRec. This hybrid model combines the SkipAwareRec with an incremental Matrix Factorisation (MF) algorithm that selects specific songs within the recommended categories. Our experiments with Spotify’s Sequential Skip Prediction Challenge dataset show that Hybrid SkipAwareRec has the potential to improve recommendations by a considerable amount with respect to the skip-agnostic MF algorithm. This strongly suggests that reformulating the next recommendations based on skips improves the quality of automatic playlists. Although in this work we focus on sequential music recommendation, our proposal can be applied to other sequential content recommendation domains, such as health for user engagement.
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Notes
- 1.
https://www.betterup.com/blog/benefits-of-music. Accessed: 2023-03-27.
- 2.
https://musicalpursuits.com/music-streaming/. Accessed: 2023-03-27.
- 3.
https://www.aicrowd.com/challenges/spotify-sequential-skip-prediction-challenge. Accessed: 2023-03-28.
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Acknowledgements
This work is co-financed by Component 5—Capitalisation and Business Innovation, integrated in the Resilience Dimension of the Recovery and Resilience Plan within the scope of the Recovery and Resilience Mechanism (MRR) of the European Union (EU), framed in the Next Generation EU, for the period 2021–2026, within project HfPT, with reference 41.
The third author contributed to the technical work in this paper while affiliated with INESC TEC and the University of Porto and to the writing of the paper while affiliated with the Joint Research Centre of the European Commission.
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Ramos, R., Oliveira, L., Vinagre, J. (2023). Hybrid SkipAwareRec: A Streaming Music Recommendation System. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14115. Springer, Cham. https://doi.org/10.1007/978-3-031-49008-8_22
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