Neural Contextual Bandits for Personalized Recommendation

Author:

Ban Yikun1ORCID,Qi Yunzhe1ORCID,He Jingrui1ORCID

Affiliation:

1. University of Illinois at Urbana-Champaign, Champaign, IL, USA

Funder

National Science Foundation

Agriculture and Food Research Initiative

Publisher

ACM

Reference39 articles.

1. Y. Abbasi-Yadkori, D. Pál, and C. Szepesvári. Improved algorithms for linear stochastic bandits. In Advances in Neural Information Processing Systems, pages 2312--2320, 2011.

2. S. Agrawal and N. Goyal. Thompson sampling for contextual bandits with linear payoffs. In International Conference on Machine Learning, pages 127--135. PMLR, 2013.

3. P. Auer, N. Cesa-Bianchi, and P. Fischer. Finite-time analysis of the multiarmed bandit problem. Machine learning, 47(2--3):235--256, 2002.

4. Generic Outlier Detection in Multi-Armed Bandit

5. Y. Ban and J. He. Convolutional neural bandit: Provable algorithm for visualaware advertising. arXiv preprint arXiv:2107.07438, 2021.

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