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Online Clustering of Contextual Cascading Bandits

arXiv 1711.08594 ↗First posted 23 Nov 2017cs.LGcited by 24

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 6; 2024: 4; 2025: 7; 2026: 2.

24 papers.

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  1. 2021Local Clustering in Contextual Multi-Armed Banditscited by 29
  2. 2021Metadata-based Multi-Task Bandits with Bayesian Hierarchical Modelscited by 24
  3. 2023Combinatorial Neural Banditscited by 16
  4. 2023Efficient Explorative Key-term Selection Strategies for Conversational Contextual Banditscited by 12
  5. 2023Online Clustering of Bandits with Misspecified User Modelscited by 12
  6. 2022Minimax Regret for Cascading Banditscited by 10
  7. 2022Federated Online Clustering of Banditscited by 10
  8. 2024Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Userscited by 6
  9. 2024Cascading Reinforcement Learningcited by 4
  10. 2024AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analyticscited by 4
  11. 2023Online Corrupted User Detection and Regret Minimizationcited by 3
  12. 2025Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contextscited by 3
12 more
  1. 2025Online Clustering of Dueling Banditscited by 3
  2. 2025Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Modelscited by 2
  3. 2023Adaptive Sample Sharing for Multi Agent Linear Banditscited by 1
  4. 2024Misalignment, Learning, and Ranking: Harnessing Users Limited Attentioncited by 1
  5. 2025Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticitycited by 1
  6. 2021On Learning to Rank Long Sequences with Contextual Banditscited by 0
  7. 2023Clustered Linear Contextual Bandits with Knapsackscited by 0
  8. 2025CoCoB: Adaptive Collaborative Combinatorial Bandits for Online Recommendationcited by 0
  9. 2025Offline Clustering of Preference Learning with Active-data Augmentationcited by 0
  10. 2025Online Learning to Rank under Corruption: A Robust Cascading Bandits Approachcited by 0
  11. 2026Steering Frozen LLMs: Adaptive Social Alignment via Online Prompt Routingcited by 0
  12. 2026DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clickscited by 0