Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 04037 | |
| Number of page(s) | 11 | |
| Section | Foundations and Frontiers in Multimodal AI, Large Models, and Generative Technologies | |
| DOI | https://doi.org/10.1051/itmconf/20257804037 | |
| Published online | 08 September 2025 | |
- Ricci, F., Rokach, L., Shapira, B.: ‘Introduction to recommender systems handbook’, in Ricci, F., Rokach, L., Shapira, B. (Eds.): ‘Recommender systems handbook’ (Springer US, 2010), pp. 1–35 [Google Scholar]
- Zangerle, E., Bauer, C.: ‘Evaluating recommender systems: survey and framework’, ACM Computing Surveys, 2022, 55, (8), pp. 1–38 [Google Scholar]
- Kaufmann, E., Bauersfeld, L., Loquercio, A., et al.: ‘Champion-level drone racing using deep reinforcement learning’, Nature, 2023, 620, (7976), pp. 982–987 [Google Scholar]
- Wu, S., Sun, F., Zhang, W., Xie, X., Cui, B.: ‘Graph neural networks in recommender systems: a survey’, ACM Computing Surveys, 2022, 55, (5), pp. 1–37 [Google Scholar]
- Hort, M., Chen, Z., Zhang, J.M., Harman, M., Sarro, F.: ‘Bias mitigation for machine learning classifiers: A comprehensive survey’, ACM Journal on Responsible Computing, 2024, 1, (2), pp. 1–52 [Google Scholar]
- McDonald, T.M., Maystre, L., Lalmas, M., Russo, D., Ciosek, K.: ‘Impatient bandits: Optimizing recommendations for the long-term without delay’. Proc. 29th ACM SIGKDD Conf. Knowledge Discovery and Data Mining, August 2023, pp. 1687–1697 [Google Scholar]
- Mu, Y., Wu, Y.: ‘Multimodal movie recommendation system using deep learning’, Mathematics, 2023, 11, (4), pp. 895 [Google Scholar]
- Patil, V., Ghalme, G., Nair, V., Narahari, Y.: ‘Achieving fairness in the stochastic multi-armed bandit problem’, Journal of Machine Learning Research, 2021, 22, (174), pp. 1–31 [Google Scholar]
- Lattimore, T., Szepesvári, C.: ‘Bandit algorithms’ (Cambridge University Press, 2020) [Google Scholar]
- Alquier, P.: ‘User-friendly introduction to PAC-Bayes bounds’, Foundations and Trends in Machine Learning, 2024, 17, (2), pp. 174–183 [Google Scholar]
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