mlsanityData to 7 Oct 2026

On Context-Dependent Clustering of Bandits

arXiv 1608.03544 ↗First posted 6 Aug 2016cs.LG cs.AI cs.IRcited by 43

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Abstract (arXiv)

We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner. CAB makes sharp departures from the state of the art by incorporating collaborative effects into inference as well as learning processes in a manner that seamlessly interleaving explore-exploit tradeoffs and collaborative steps. We prove regret bounds under various assumptions on the data, which exhibit a crisp dependence on the expected number of clusters over the users, a natural measure of the statistical difficulty of the learning task. Experiments on production and real-world datasets show that CAB offers significantly increased prediction performance against a representative pool of state-of-the-art methods.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Aug 2016.

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Citing papers per year: 2021: 8; 2022: 9; 2023: 14; 2024: 6; 2025: 5; 2026: 1.

43 papers.

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  1. 2021RL for Latent MDPs: Regret Guarantees and a Lower Boundcited by 69
  2. 2021Federated Linear Contextual Banditscited by 61
  3. 2021Hierarchical Bayesian Banditscited by 33
  4. 2021Local Clustering in Contextual Multi-Armed Banditscited by 29
  5. 2021Multi-facet Contextual Bandits: A Neural Network Perspectivecited by 20
  6. 2023Graph Neural Banditscited by 20
  7. 2023Online Clustering of Bandits with Misspecified User Modelscited by 12
  8. 2023Optimal Algorithms for Latent Bandits with Cluster Structurecited by 11
  9. 2022Federated Online Clustering of Banditscited by 10
  10. 2023Neural Contextual Bandits for Personalized Recommendationcited by 10
  11. 2022Private and Byzantine-Proof Cooperative Decision-Makingcited by 9
  12. 2022Neural Bandit with Arm Group Graphcited by 8
31 more
  1. 2022Neural Collaborative Filtering Bandits via Meta Learningcited by 7
  2. 2023Evaluating Online Bandit Exploration In Large-Scale Recommender Systemcited by 7
  3. 2023Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneitycited by 7
  4. 2024GPT4Rec: Graph Prompt Tuning for Streaming Recommendationcited by 7
  5. 2022Breaking the √T Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Banditscited by 5
  6. 2024RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluationcited by 5
  7. 2024Linear Contextual Bandits with Hybrid Payoff: Revisitedcited by 5
  8. 2021Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Banditscited by 4
  9. 2022Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanismscited by 4
  10. 2023Prospective Side Information for Latent MDPscited by 4
  11. 2025Generalized Low-Rank Matrix Contextual Bandits with Graph Informationcited by 4
  12. 2023Context-lumpable stochastic banditscited by 3
  13. 2024Expert with Clustering: Hierarchical Online Preference Learning Frameworkcited by 3
  14. 2024Federated Linear Contextual Bandits with Heterogeneous Clientscited by 3
  15. 2025Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contextscited by 3
  16. 2023Leveraging heterogeneous spillover in maximizing contextual bandit rewardscited by 2
  17. 2023Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraintscited by 2
  18. 2025Online Clustering of Dueling Banditscited by 2
  19. 2022A Scalable Recommendation Engine for New Users and Itemscited by 1
  20. 2022Anonymous Bandits for Multi-User Systemscited by 1
  21. 2023Adaptive Sample Sharing for Multi Agent Linear Banditscited by 1
  22. 2024Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensitycited by 1
  23. 2025Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticitycited by 1
  24. 2021When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solutioncited by 0
  25. 2021Margin-Independent Online Multiclass Learning via Convex Geometrycited by 0
  26. 2022Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimizationcited by 0
  27. 2023Stochastic Contextual Bandits with Graph-based Contextscited by 0
  28. 2023Clustered Linear Contextual Bandits with Knapsackscited by 0
  29. 2023Adaptive Interventions with User-Defined Goals for Health Behavior Changecited by 0
  30. 2025CoCoB: Adaptive Collaborative Combinatorial Bandits for Online Recommendationcited by 0
  31. 2026Spectral banditscited by 0