DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks
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Computer Science > Machine Learning
Title:DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks
Abstract:In this work, we extend the Dependent Click Model (DCM) Bandits to a multiplayer information-asymmetric setting, where multiple agents interact with a shared ranked list and may observe multiple clicks per session, introducing new challenges for selection strategies. We study asymmetry in (1) actions and (2) rewards, providing sublinear regret guarantees for three settings where at least one asymmetry is present. Establishing matching information-theoretic lower bounds for these settings is left as an open problem. We further show that for small termination probabilities, the termination ranking need not be known, improving on prior single-agent results. Experiments confirm that our algorithms perform well across asymmetric environments and highlight the critical role of feedback structure, specifically the distinction between full versus first-click feedback, in coordinating exploration and minimizing regret.
| Comments: | Accepted to the Asia Conference on Machine Learning and Computing (ACMLC 2026) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.11873 [cs.LG] |
| (or arXiv:2608.11873v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.11873
arXiv-issued DOI via DataCite (pending registration)
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