# Online Clustering of Contextual Cascading Bandits

arXiv: https://arxiv.org/abs/1711.08594 · HTML: https://mlsanity.com/paper/1711.08594/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2017.

## Built on by (most cited first, 24 of 24)

- [Local Clustering in Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2103.00063/) (2021)
- [Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models](https://mlsanity.com/paper/2108.06422/) (2021)
- [Combinatorial Neural Bandits](https://mlsanity.com/paper/2306.00242/) (2023)
- [Efficient Explorative Key-term Selection Strategies for Conversational Contextual Bandits](https://mlsanity.com/paper/2303.00315/) (2023)
- [Online Clustering of Bandits with Misspecified User Models](https://mlsanity.com/paper/2310.02717/) (2023)
- [Minimax Regret for Cascading Bandits](https://mlsanity.com/paper/2203.12577/) (2022)
- [Federated Online Clustering of Bandits](https://mlsanity.com/paper/2208.14865/) (2022)
- [Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users](https://mlsanity.com/paper/2402.16312/) (2024)
- [Cascading Reinforcement Learning](https://mlsanity.com/paper/2401.08961/) (2024)
- [AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics](https://mlsanity.com/paper/2407.20124/) (2024)
- [Online Corrupted User Detection and Regret Minimization](https://mlsanity.com/paper/2310.04768/) (2023)
- [Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts](https://mlsanity.com/paper/2501.00891/) (2025)
- [Online Clustering of Dueling Bandits](https://mlsanity.com/paper/2502.02079/) (2025)
- [Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models](https://mlsanity.com/paper/2502.08003/) (2025)
- [Adaptive Sample Sharing for Multi Agent Linear Bandits](https://mlsanity.com/paper/2309.08710/) (2023)
- [Misalignment, Learning, and Ranking: Harnessing Users Limited Attention](https://mlsanity.com/paper/2402.14013/) (2024)
- [Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity](https://mlsanity.com/paper/2506.12389/) (2025)
- [On Learning to Rank Long Sequences with Contextual Bandits](https://mlsanity.com/paper/2106.03546/) (2021)
- [Clustered Linear Contextual Bandits with Knapsacks](https://mlsanity.com/paper/2308.10722/) (2023)
- [CoCoB: Adaptive Collaborative Combinatorial Bandits for Online Recommendation](https://mlsanity.com/paper/2505.03840/) (2025)
- [Offline Clustering of Preference Learning with Active-data Augmentation](https://mlsanity.com/paper/2510.26301/) (2025)
- [Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach](https://mlsanity.com/paper/2511.03074/) (2025)
- [Steering Frozen LLMs: Adaptive Social Alignment via Online Prompt Routing](https://mlsanity.com/paper/2603.15647/) (2026)
- [DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks](https://mlsanity.com/paper/2608.11873/) (2026)

## Abstract (arXiv)

We consider a new setting of online clustering of contextual cascading bandits, an online learning problem where the underlying cluster structure over users is unknown and needs to be learned from a random prefix feedback. More precisely, a learning agent recommends an ordered list of items to a user, who checks the list and stops at the first satisfactory item, if any. We propose an algorithm of CLUB-cascade for this setting and prove a T-step regret bound of order tildeO(√T). Previous work corresponds to the degenerate case of only one cluster, and our general regret bound in this special case also significantly improves theirs. We conduct experiments on both synthetic and real data, and demonstrate the effectiveness of our algorithm and the advantage of incorporating online clustering method.