# Local Clustering in Contextual Multi-Armed Bandits

arXiv: https://arxiv.org/abs/2103.00063 · HTML: https://mlsanity.com/paper/2103.00063/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [On Context-Dependent Clustering of Bandits](https://mlsanity.com/paper/1608.03544/) (2016)
- [Improved Algorithm on Online Clustering of Bandits](https://mlsanity.com/paper/1902.09162/) (2019)
- [Heat kernel based community detection](https://mlsanity.com/paper/1403.3148/) (2014)
- [Balanced Linear Contextual Bandits](https://mlsanity.com/paper/1812.06227/) (2018)
- [Online Clustering of Contextual Cascading Bandits](https://mlsanity.com/paper/1711.08594/) (2017)
- [Generic Outlier Detection in Multi-Armed Bandit](https://mlsanity.com/paper/2007.07293/) (2020)
- [Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners](https://mlsanity.com/paper/1804.06481/) (2018)
- [No Place to Hide: Catching Fraudulent Entities in Tensors](https://mlsanity.com/paper/1810.06230/) (2018)

## Built on by (most cited first, 29 of 29)

- [EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits](https://mlsanity.com/paper/2110.03177/) (2021)
- [Multi-facet Contextual Bandits: A Neural Network Perspective](https://mlsanity.com/paper/2106.03039/) (2021)
- [Graph Neural Bandits](https://mlsanity.com/paper/2308.10808/) (2023)
- [Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization](https://mlsanity.com/paper/2506.17252/) (2025)
- [PageRank Bandits for Link Prediction](https://mlsanity.com/paper/2411.01410/) (2024)
- [Online Clustering of Bandits with Misspecified User Models](https://mlsanity.com/paper/2310.02717/) (2023)
- [Improved Algorithms for Neural Active Learning](https://mlsanity.com/paper/2210.00423/) (2022)
- [Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards](https://mlsanity.com/paper/2602.08499/) (2026)
- [Neural Contextual Bandits for Personalized Recommendation](https://mlsanity.com/paper/2312.14037/) (2023)
- [Neural Active Learning Beyond Bandits](https://mlsanity.com/paper/2404.12522/) (2024)
- [Neural Bandit with Arm Group Graph](https://mlsanity.com/paper/2206.03644/) (2022)
- [Max-Min Grouped Bandits](https://mlsanity.com/paper/2111.08862/) (2021)
- [Neural Collaborative Filtering Bandits via Meta Learning](https://mlsanity.com/paper/2201.13395/) (2022)
- [Neural Exploitation and Exploration of Contextual Bandits](https://mlsanity.com/paper/2305.03784/) (2023)
- [GPT4Rec: Graph Prompt Tuning for Streaming Recommendation](https://mlsanity.com/paper/2406.08229/) (2024)
- [Meta Clustering of Neural Bandits](https://mlsanity.com/paper/2408.05586/) (2024)
- [Influence Maximization via Graph Neural Bandits](https://mlsanity.com/paper/2406.12835/) (2024)
- [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)
- [Leveraging heterogeneous spillover in maximizing contextual bandit rewards](https://mlsanity.com/paper/2310.10259/) (2023)
- [Adaptive Sample Sharing for Multi Agent Linear Bandits](https://mlsanity.com/paper/2309.08710/) (2023)
- [Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity](https://mlsanity.com/paper/2506.12389/) (2025)
- [Convolutional Neural Bandit for Visual-aware Recommendation](https://mlsanity.com/paper/2107.07438/) (2021)
- [Clustered Linear Contextual Bandits with Knapsacks](https://mlsanity.com/paper/2308.10722/) (2023)
- [Empathic Responding for Digital Interpersonal Emotion Regulation via Content Recommendation](https://mlsanity.com/paper/2408.07704/) (2024)
- [The Nah Bandit: Modeling User Non-compliance in Recommendation Systems](https://mlsanity.com/paper/2408.07897/) (2024)
- [Estimating Causal Effects in Networks with Cluster-Based Bandits](https://mlsanity.com/paper/2505.04200/) (2025)
- [Near Optimal Best Arm Identification for Clustered Bandits](https://mlsanity.com/paper/2505.10147/) (2025)
- [Clus-UCB: A Near-Optimal Algorithm for Clustered Bandits](https://mlsanity.com/paper/2508.02909/) (2025)

## Abstract (arXiv)

We study identifying user clusters in contextual multi-armed bandits (MAB). Contextual MAB is an effective tool for many real applications, such as content recommendation and online advertisement. In practice, user dependency plays an essential role in the user's actions, and thus the rewards. Clustering similar users can improve the quality of reward estimation, which in turn leads to more effective content recommendation and targeted advertising. Different from traditional clustering settings, we cluster users based on the unknown bandit parameters, which will be estimated incrementally. In particular, we define the problem of cluster detection in contextual MAB, and propose a bandit algorithm, LOCB, embedded with local clustering procedure. And, we provide theoretical analysis about LOCB in terms of the correctness and efficiency of clustering and its regret bound. Finally, we evaluate the proposed algorithm from various aspects, which outperforms state-of-the-art baselines.