Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 01037 | |
| Number of page(s) | 10 | |
| Section | Deep Learning and Reinforcement Learning – Theories and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20257801037 | |
| Published online | 08 September 2025 | |
- Silva, N., Werneck, H., Silva, T., Pereira, A.C., and Rocha, L.: 'Multi-armed bandits in recommendation systems: A survey of the state-of-the-art and future directions', Expert Syst. Appl., 2022, 197, pp. 116669 [CrossRef] [Google Scholar]
- Zhao, Z.: 'Analysis on the "Douyin (TikTok) Mania" phenomenon based on recommendation algorithms'. Proc. E3S Web of Conferences, 2021, 235, pp. 03029 [Google Scholar]
- Hagar, N., and Diakopoulos, N.: 'Algorithmic indifference: The dearth of news recommendations on TikTok', New Media Soc., 2023, pp. 14614448231192964 [Google Scholar]
- Zhang, M., and Liu, Y.: 'A commentary of TikTok recommendation algorithms in MIT Technology Review 2021', Fundam. Res., 2021, 1, (6), pp. 846–847 [Google Scholar]
- Pilani, A., Mathur, K., Agrawald, H., Chandola, D., Tikkiwal, V.A., and Kumar, A.: 'Contextual Bandit Approach-based Recommendation System for Personalized Web-based Services', Appl. Artif. Intell., 2021, 35, (7), pp. 489–504 [Google Scholar]
- Yan, C., Han, H., Wang, Z., and Zhang, Y.: 'Two-phase multi-armed bandit for online recommendation'. Proc. IEEE 8th Int. Conf. Data Science and Advanced Analytics (DSAA), Oct. 2021, pp. 1–8 [Google Scholar]
- Zhao, J.: 'Comparison of Multi-Armed Bandit Algorithms in Advertising Recommendation Systems', Appl. Comput. Eng., 2024, 83, pp. 62–71 [Google Scholar]
- Xu, S.: 'BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System', arXiv preprint arXiv:2106.10898, 2021 [Google Scholar]
- Cunha, T., and Marchini, A.: 'A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization', arXiv preprint arXiv:2409.08752, 2024 [Google Scholar]
- Wang, X., Ounis, I., and Macdonald, C.: 'BanditProp: Bandit selection of review properties for effective recommendation', ACM Trans. Web, 2022, 16, (4), pp. 1–19 [Google Scholar]
- Tan, Y., and Yoon, S.: 'Testing the effects of personalized recommendation service, filter bubble and big data attitude on continued use of TikTok', Asia Pac. J. Mark. Logist., 2024 [Google Scholar]
- Chen, Y., and Huang, J.: 'Effective content recommendation in new media: Leveraging algorithmic approaches', IEEE Access, 2024 [Google Scholar]
- [de Campos Silva, N.: 'Active learning in contextual bandits: handling the uncertainty about the user's preferences in interactive recommendation systems', 2023 [Google Scholar]
- Cañamares, R., Redondo, M., and Castells, P.: 'Multi-armed recommender system bandit ensembles'. Proc. 13th ACM Conf. Recommender Systems, Sept. 2019, pp. 432–436 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.