Multi-User Contextual Cascading Bandits for Personalized Recommendation

TL;DR

This paper introduces a new multi-user bandit model for personalized recommendations, proposing algorithms with provable regret bounds and demonstrating their effectiveness through experiments.

Contribution

It develops the Multi-User Contextual Cascading Bandit model and proposes two algorithms with theoretical guarantees, addressing simultaneous multi-user interactions in recommendation systems.

Findings

UCBBP achieves regret of O(\

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Abstract

We introduce a Multi-User Contextual Cascading Bandit model, a new combinatorial bandit framework that captures realistic online advertising scenarios where multiple users interact with sequentially displayed items simultaneously. Unlike classical contextual bandits, MCCB integrates three key structural elements: (i) cascading feedback based on sequential arm exposure, (ii) parallel context sessions enabling selective exploration, and (iii) heterogeneous arm-level rewards. We first propose Upper Confidence Bound with Backward Planning (UCBBP), a UCB-style algorithm tailored to this setting, and prove that it achieves a regret bound of $\widetilde{O}(\sqrt{THN})$ over $T$ episodes, $H$ session steps, and $N$ contexts per episode. Motivated by the fact that many users interact with the system simultaneously, we introduce a second algorithm, termed Active Upper Confidence Bound with Backward Planning (AUCBBP), which shows a strict efficiency improvement in context scaling, i.e., user scaling, with a regret bound of $\widetilde{O}(\sqrt{T+HN})$. We validate our theoretical findings via numerical experiments, demonstrating the empirical effectiveness of both algorithms under various settings.