Multi-facet Contextual Bandits: A Neural Network Perspective

Yikun Ban, Jingrui He, Curtiss B. Cook

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 Õ((K + 1) gšT) 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.

Original languageEnglish (US)
Title of host publicationKDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Electronic)9781450383325
StatePublished - Aug 14 2021
Event27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore
Duration: Aug 14 2021Aug 18 2021

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining


Conference27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
CityVirtual, Online


  • contextual bandits
  • neural network
  • regret analysis

ASJC Scopus subject areas

  • Software
  • Information Systems


Dive into the research topics of 'Multi-facet Contextual Bandits: A Neural Network Perspective'. Together they form a unique fingerprint.

Cite this