多用户环境下，带多重点击的推荐系统优化方法。
DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks
- 设计多玩家信息不对称下的点击建模算法。
- 在多种不对称场景中实现次线性后悔率。
- 适用于需区分首次点击与全部点击反馈的场景。
本文将依赖点击模型（DCM）扩展至多玩家信息不对称场景，多个智能体共享一个排序列表并可能产生多次点击，带来新的选择策略挑战。研究了行动与奖励两方面的不对称性，在至少存在一种不对称性的三种设置下提供了次线性后悔保证。这些设置的信息论下界尚未完全建立，仍为开放问题。此外，我们证明当终止概率较小时，无需知晓终止排序，优于先前单智能体结果。实验验证了算法在各类不对称环境中的良好表现，并强调反馈结构的关键作用，特别是全量点击与首次点击反馈的区别，对探索与后悔最小化至关重要。
原文摘要 · Abstract (English)
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.
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