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Applying Multi-Armed Bandit algorithms for music recommendations at Spotify
- 1 The School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
* Author to whom correspondence should be addressed.
Abstract
This study explores the application of multi-armed bandit algorithms in enhancing music recommendation systems, with a focus on Spotify. It delves into the Explore-Then-Commit (ETC), Upper Confidence Bound (UCB), and Thompson Sampling (TS) algorithms, evaluating their efficacy within the Spotify context. The primary objective is to determine which algorithm optimally balances exploration and exploitation to maximize user satisfaction and engagement. The research reveals that the ETC algorithm, with its rigid exploration and exploitation phases, incurs a notably higher regret value. This rigidity can lead to missed opportunities in identifying optimal choices and hinder adaptability to evolving user preferences. Conversely, the UCB and TS algorithms exhibit remarkable adaptability and a flexible balance between exploration and exploitation. This flexibility translates into more personalized and satisfactory user experiences in music recommendations. However, the selection of the most appropriate algorithm should be contingent on the size and characteristics of the specific user dataset, as well as the fine-tuning of algorithm parameters to align with user preferences and behaviors.
Keywords
multi-armed bandit algorithm, music recommendation system, average regret
[1]. Silva, N., Werneck, H., Silva, T., Pereira, A. C., & Rocha, L. (2022). Multi-armed bandits in recommendation systems: A survey of the state-of-the-art and future directions. Expert Systems with Applications, 197, 116669.
[2]. Romano, G., Agostini, A., Trovò, F., Gatti, N., & Restelli, M. (2022). Multi-armed bandit problem with temporally-partitioned rewards: When partial feedback counts. arxiv preprint arxiv:2206.00586.
[3]. Elena, G., Milos, K., & Eugene, I. (2021). Survey of multiarmed bandit algorithms applied to recommendation systems. International Journal of Open Information Technologies, 9(4), 12-27.
[4]. Zhu, X., Xu, H., Zhao, Z., & others. (2021). An Environmental Intrusion Detection Technology Based on WiFi. Wireless Personal Communications, 119(2), 1425-1436.
[5]. Mehrotra, R., Xue, N., & Lalmas, M. (2020, August). Bandit based optimization of multiple objectives on a music streaming platform. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 3224-3233).
[6]. Agostini, A. (2021). Multi-armed bandit with persistent reward.
[7]. Moerchen, F., Ernst, P., & Zappella, G. (2020, October). Personalizing natural language understanding using multi-armed bandits and
[8]. Bendada, W., Salha, G., & Bontempelli, T. (2020, September). Carousel personalization in music streaming apps with contextual bandits. In Proceedings of the 14th ACM Conference on Recommender Systems (pp. 420-425).
[9]. Pereira, B. L., Ueda, A., Penha, G., Santos, R. L., & Ziviani, N. (2019, September). Online learning to rank for sequential music recommendation. In Proceedings of the 13th ACM Conference on Recommender Systems (pp. 237-245).
[10]. Jones, B., Brennan, J., Chen, Y., & Filipek, J. (2021). Multi-Armed Bandits with Non-Stationary Means.
Cite this article
Xia,Y. (2024). Applying Multi-Armed Bandit algorithms for music recommendations at Spotify. Applied and Computational Engineering,68,54-64.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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