Improvement of the recommendation system based on the multi-armed bandit algorithm

Research Article
Open access

Improvement of the recommendation system based on the multi-armed bandit algorithm

Youxuan Li 1*
  • 1 Tianjin Yaohua High School    
  • *corresponding author yyol0417@163.com
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/36/20230453
ACE Vol.36
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-297-8
ISBN (Online): 978-1-83558-298-5

Abstract

In order to effectively solve common problems of the recommendation system, such as the cold start problem and dynamic data modeling problem, the multi-armed bandit (MAB) algorithm, the collaborative filtering (CF) algorithm, and the user information feedback are applied by researchers to update the recommendation model online and in time. In other words, the cold start problem of the recommendation system is transformed into an issue of exploration and utilization. The MAB algorithm is used, user features are introduced as content, and the synergy between users is further considered. In this paper, the author studies the improvement of the recommendation system based on the multi-armed bandit algorithm. The Liner Upper Confidence Bound (LinUCB), Collaborative Filtering Bandits (COFIBA), and Context-Aware clustering of Bandits (CAB) algorithms are analyzed. It is found that the MAB algorithm can get a good maximum total revenue regardless of the content value after going through the cold start stage. In the case of a particularly large amount of content, the CAB algorithm achieves the greatest effect.

Keywords:

recommendation system, multi-armed bandit machine, LinUCB, COFIBA, CAB

Li,Y. (2024). Improvement of the recommendation system based on the multi-armed bandit algorithm. Applied and Computational Engineering,36,237-241.
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References

[1]. Ni, H., Xu, H., Ma, D. and Fan, J. (2023). Contextual combinatorial bandit on portfolio management. Expert Systems With Applications, 221, 119677. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2023.119677.

[2]. Mandai, Y. and Kaneko, T. (2016). LinUCB applied to Monte Carlo tree search, Theoretical Computer Science, 644, 114-126, ISSN 0304-3975, https://doi.org/10.1016/j.tcs.2016.06.035.

[3]. Bouneffouf, D. (2013). Exponentiated Gradient LINUCB for Contextual Multi-Armed Bandits. https://doi.org/10.48550/arXiv.1305.2415.

[4]. Xiang, J. H., Wei, J. H. and Guo, H. (2019). An Improved Blind Adaptive Beamforming CAB Algorithm Based on Fireworks Algorithm is Presented. In Proceedings of the 2019 3rd International Conference on Digital Signal Processing (ICDSP '19). Association for Computing Machinery, New York, NY, USA, 69-74. https://doi.org/10.1145/3316551.3316560.

[5]. Shimizu, N., Ohta, K., Nitta, M., Inoue, N., Yonemoto, N., Nonogi, H., Nagao, K. and Kimura, T. (2013). Implementation of the Combination of CAB Algorithm and CC-Only CPR Does Not Worsen the Outcomes of Paediatric Out-of-Hospital Cardiac Arrests: Nation Wide Population Based Study. Scientific Sessions and Resuscitation Science, 128(22).

[6]. Lu, D., Wu, R., Su, Z., et al. (2006). A Novel Robust Cyclic Adaptive Beamforming Algorithm. The Chinese Institute of Electronics (CIE). Proceedings of 2006 8th International Conference on Signal Processing (Volume Ⅰ of Ⅳ). Institute of Electrical and Electronics Engineers, 516-519.

[7]. CSDN. (2018). exploration-exploitation algorithm in the recommendation system. https://blog.csdn.net/BertDai/article/details/79056555.

[8]. Beaudoin, M. A. and Boulet, B. (2022). Improving gearshift controllers for electric vehicles with reinforcement learning, Mechanism and Machine Theory, 169, 104654, ISSN 0094-114X. https://doi.org/10.1016/j.mechmachtheory.2021.104654.

[9]. Dutta, H. and Biswas, S. K. (2021). Distributed Reinforcement Learning for scalable wireless medium access in IoTs and sensor networks. Comput. Networks, 202, 108662.

[10]. Wei, X., Xiang, Y., Li, J. and Liu, J. (2022). Wind power bidding coordinated with energy storage system operation in real-time electricity market: A maximum entropy deep reinforcement learning approach. Energy Reports, 8(S1).

Cite this article

Li,Y. (2024). Improvement of the recommendation system based on the multi-armed bandit algorithm. Applied and Computational Engineering,36,237-241.

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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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-297-8(Print) / 978-1-83558-298-5(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.36
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Ni, H., Xu, H., Ma, D. and Fan, J. (2023). Contextual combinatorial bandit on portfolio management. Expert Systems With Applications, 221, 119677. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2023.119677.

[2]. Mandai, Y. and Kaneko, T. (2016). LinUCB applied to Monte Carlo tree search, Theoretical Computer Science, 644, 114-126, ISSN 0304-3975, https://doi.org/10.1016/j.tcs.2016.06.035.

[3]. Bouneffouf, D. (2013). Exponentiated Gradient LINUCB for Contextual Multi-Armed Bandits. https://doi.org/10.48550/arXiv.1305.2415.

[4]. Xiang, J. H., Wei, J. H. and Guo, H. (2019). An Improved Blind Adaptive Beamforming CAB Algorithm Based on Fireworks Algorithm is Presented. In Proceedings of the 2019 3rd International Conference on Digital Signal Processing (ICDSP '19). Association for Computing Machinery, New York, NY, USA, 69-74. https://doi.org/10.1145/3316551.3316560.

[5]. Shimizu, N., Ohta, K., Nitta, M., Inoue, N., Yonemoto, N., Nonogi, H., Nagao, K. and Kimura, T. (2013). Implementation of the Combination of CAB Algorithm and CC-Only CPR Does Not Worsen the Outcomes of Paediatric Out-of-Hospital Cardiac Arrests: Nation Wide Population Based Study. Scientific Sessions and Resuscitation Science, 128(22).

[6]. Lu, D., Wu, R., Su, Z., et al. (2006). A Novel Robust Cyclic Adaptive Beamforming Algorithm. The Chinese Institute of Electronics (CIE). Proceedings of 2006 8th International Conference on Signal Processing (Volume Ⅰ of Ⅳ). Institute of Electrical and Electronics Engineers, 516-519.

[7]. CSDN. (2018). exploration-exploitation algorithm in the recommendation system. https://blog.csdn.net/BertDai/article/details/79056555.

[8]. Beaudoin, M. A. and Boulet, B. (2022). Improving gearshift controllers for electric vehicles with reinforcement learning, Mechanism and Machine Theory, 169, 104654, ISSN 0094-114X. https://doi.org/10.1016/j.mechmachtheory.2021.104654.

[9]. Dutta, H. and Biswas, S. K. (2021). Distributed Reinforcement Learning for scalable wireless medium access in IoTs and sensor networks. Comput. Networks, 202, 108662.

[10]. Wei, X., Xiang, Y., Li, J. and Liu, J. (2022). Wind power bidding coordinated with energy storage system operation in real-time electricity market: A maximum entropy deep reinforcement learning approach. Energy Reports, 8(S1).

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