Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 04027 | |
| Number of page(s) | 10 | |
| Section | Foundations and Frontiers in Multimodal AI, Large Models, and Generative Technologies | |
| DOI | https://doi.org/10.1051/itmconf/20257804027 | |
| Published online | 08 September 2025 | |
- Zhang, E., Ma, W., Zhang, J., et al.: ‘A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning’. Electronics, 2023, 12, (24), 5034. [Google Scholar]
- Fenglin, Lu.: ‘Adaptive Recommendation Sym Strategies: An Exploration of Online Machine Learning Algorithms’, EMITI, 2024, pp. 447–451 [Google Scholar]
- Elena, G., Milos, K., Eugene, I.: ‘Survey of multiarmed bandit algorithms applied to recommendation systems’, Int. J. Open Inf. Technol., 2021, 9, (4), pp. 12–27 [Google Scholar]
- Singh, S., Mahmood, A.: ‘The NLP Cookbook: Modern Recipes for Transformer Based Deep Learning Architectures’, IEEE Access, 2021, 9, pp. 68675–68702 [CrossRef] [Google Scholar]
- Jin, H.: ‘Optimizing Short Video Recommendation Systems: Addressing Cold-Start and Diversity Challenges through Advanced Algorithms’, Appl. Comput. Eng., 2024, 83, pp. 72–80 [Google Scholar]
- Song, Y.: ‘Truncated LinUCB for Stochastic Linear Bandits’. PhD thesis, Queen’s University, 2022 [Google Scholar]
- Guo, H., Naeff, R., Nikulkov, A., et al.: ‘Evaluating Online Bandit Exploration in Large-Scale Recommender System’. PhD thesis, Stanford University, 2023 [Google Scholar]
- Lattimore, T., Szepesvári, C.: ‘Bandit algorithms’ (Cambridge University Press, 2020) [Google Scholar]
- De Ath, G., Everson, R.M., Fieldsend, J.E.: ‘Asynchronous ε-Greedy Bayesian Optimisation’. Proc. 37th Conf. Uncertainty in Artificial Intelligence, Exeter, United Kingdom, June 2021, pp. 578–588 [Google Scholar]
- Du, M.: ‘Applications and Advances of UCB Algorithms in Dynamic and Contaminated Environments’, Appl. Comput. Eng., 2024, 83, pp. 155–165 [Google Scholar]
- Agrawal, S., Goyal, N.: ‘Analysis of Thompson sampling’. Proc. 25th Annual Conf. Learning Theory, Edinburgh, UK, June 2012, pp. 39.1–39.26 [Google Scholar]
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