Contextual Multinomial Logit Bandits with General Value Functions
<|reference_start|>Contextual Multinomial Logit Bandits with General Value Functions: Contextual multinomial logit (MNL) bandits capture many real-world assortment recommendation problems such as online retailing/advertising. However, prior work has only considered (generalized) linear value functions, which greatly limits its applicability. Motivated by this fact, in this work, we consider contextual MNL bandits with a general value function class that contains the ground truth, borrowing ideas from a recent trend of studies on contextual bandits. Specifically, we consider both the stochastic and the adversarial settings, and propose a suite of algorithms, each with different computation-regret trade-off. When applied to the linear case, our results not only are the first ones with no dependence on a certain problem-dependent constant that can be exponentially large, but also enjoy other advantages such as computational efficiency, dimension-free regret bounds, or the ability to handle completely adversarial contexts and rewards.<|reference_end|>
@article{zhang2024contextual,
    title={Contextual Multinomial Logit Bandits with General Value Functions},
    author={Mengxiao Zhang, Haipeng Luo},
    journal={arXiv preprint arXiv:2402.08126},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08126},
    primaryClass={cs.LG}
}