mlsanityData to 7 Oct 2026

Multi-facet Contextual Bandits: A Neural Network Perspective

arXiv 2106.03039 ↗First posted 6 Jun 2021cs.LGcited by 20

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Abstract (arXiv)

Contextual multi-armed bandit has shown to be an effective tool in recommender systems. In this paper, we study a novel problem of multi-facet bandits involving a group of bandits, each characterizing the users' needs from one unique aspect. In each round, for the given user, we need to select one arm from each bandit, such that the combination of all arms maximizes the final reward. This problem can find immediate applications in E-commerce, healthcare, etc. To address this problem, we propose a novel algorithm, named MuFasa, which utilizes an assembled neural network to jointly learn the underlying reward functions of multiple bandits. It estimates an Upper Confidence Bound (UCB) linked with the expected reward to balance between exploitation and exploration. Under mild assumptions, we provide the regret analysis of MuFasa. It can achieve the near-optimal widetilde O((K+1)√T) regret bound where K is the number of bandits and T is the number of played rounds. Furthermore, we conduct extensive experiments to show that MuFasa outperforms strong baselines on real-world data sets.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 20 of its 48 references that are in the map. First posted on arXiv 6 Jun 2021.

Builds on

20 of 48 references.

References · 20

  1. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
  2. 2018A Convergence Theory for Deep Learning via Over-Parameterizationcited by 784
  3. 2019On Exact Computation with an Infinitely Wide Neural Netcited by 572
  4. 2019Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networkscited by 221
  5. 2019Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Boundcited by 199
  6. 2013Finite-Time Analysis of Kernelised Contextual Banditscited by 177
  7. 2018Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Samplingcited by 166
  8. 2014Online Clustering of Banditscited by 99
  9. 2021HDMI: High-order Deep Multiplex Infomaxcited by 82
  10. 2014Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Armscited by 78
  11. 2018Efficient Exploration through Bayesian Deep Q-Networkscited by 67
  12. 2017Multi-Task Learning for Contextual Banditscited by 47
8 more
  1. 2016On Context-Dependent Clustering of Banditscited by 43
  2. 2017BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systemscited by 30
  3. 2018Balanced Linear Contextual Banditscited by 30
  4. 2021Local Clustering in Contextual Multi-Armed Banditscited by 29
  5. 2014A Neural Networks Committee for the Contextual Bandit Problemcited by 23
  6. 2021Deep Co-Attention Network for Multi-View Subspace Learningcited by 17
  7. 2019Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matchingcited by 16
  8. 2020Generic Outlier Detection in Multi-Armed Banditcited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 3; 2024: 6; 2025: 3; 2026: 1.

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  1. 2025MixLLM: Dynamic Routing in Mixed Large Language Modelscited by 56
  2. 2021EE-Net: Exploitation-Exploration Neural Networks in Contextual Banditscited by 43
  3. 2025VideoScene: Distilling Video Diffusion Model to Generate 3D Scenes in One Stepcited by 27
  4. 2023Graph Neural Banditscited by 20
  5. 2024PageRank Bandits for Link Predictioncited by 14
  6. 2022Improved Algorithms for Neural Active Learningcited by 11
  7. 2023Neural Contextual Bandits for Personalized Recommendationcited by 10
  8. 2024Neural Active Learning Beyond Banditscited by 10
  9. 2022DISCO: Comprehensive and Explainable Disinformation Detectioncited by 9
  10. 2022Neural Bandit with Arm Group Graphcited by 8
  11. 2022Neural Collaborative Filtering Bandits via Meta Learningcited by 7
  12. 2023Neural Exploitation and Exploration of Contextual Banditscited by 7
8 more
  1. 2024Meta Clustering of Neural Banditscited by 7
  2. 2021Adversarial Gradient Driven Exploration for Deep Click-Through Rate Predictioncited by 4
  3. 2024Influence Maximization via Graph Neural Banditscited by 4
  4. 2024Neural Combinatorial Clustered Bandits for Recommendation Systemscited by 2
  5. 2025Resource-efficient Inference with Foundation Model Programscited by 2
  6. 2024Online GPU Energy Optimization with Switching-Aware Banditscited by 1
  7. 2021Convolutional Neural Bandit for Visual-aware Recommendationcited by 0
  8. 2026Learning to Attack: A Bandit Approach to Adversarial Context Poisoningcited by 0