Multi-User Contextual Cascading Bandits for Personalized Recommendation

• URL: http://arxiv.org/abs/2508.13981v2
• Date: Sun, 24 Aug 2025 18:18:21 GMT
• Title: Multi-User Contextual Cascading Bandits for Personalized Recommendation
• Authors: Jiho Park, Huiwen Jia,
• Abstract summary: Multi-User Contextual Cascading Bandit model captures realistic online advertising scenarios where multiple users interact with sequentially displayed items simultaneously.<n>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 $widetildeO(sqrtTHN)$ over $T$ episodes, $H$ session steps, and $N$ contexts per episode.<n>Motivated by the fact that many users interact with the system simultaneously, we introduce a second algorithm, termed Active Upper Confidence Bound with
• Score: 2.506355754272765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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