Meta Clustering of Neural Bandits

Yikun Ban, Yunzhe Qi, Tianxin Wei, Lihui Liu, Jingrui He

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

Abstract

The contextual bandit has been identified as a powerful framework to formulate the recommendation process as a sequential decision-making process, where each item is regarded as an arm and the objective is to minimize the regret of T rounds. In this paper, we study a new problem, Clustering of Neural Bandits, by extending previous work to the arbitrary reward function, to strike a balance between user heterogeneity and user correlations in the recommender system. To solve this problem, we propose a novel algorithm called M-CNB, which utilizes a meta-learner to represent and rapidly adapt to dynamic clusters, along with an informative Upper Confidence Bound (UCB)-based exploration strategy. We provide an instance-dependent performance guarantee for the proposed algorithm that withstands the adversarial context, and we further prove the guarantee is at least as good as state-of-the-art (SOTA) approaches under the same assumptions. In extensive experiments conducted in both recommendation and online classification scenarios, M-CNB outperforms SOTA baselines. This shows the effectiveness of the proposed approach in improving online recommendation and online classification performance.

Original languageEnglish (US)
Title of host publicationKDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages95-106
Number of pages12
ISBN (Electronic)9798400704901
DOIs
StatePublished - Aug 25 2024
Event30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024 - Barcelona, Spain
Duration: Aug 25 2024Aug 29 2024

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ISSN (Print)2154-817X

Conference

Conference30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024
Country/TerritorySpain
CityBarcelona
Period8/25/248/29/24

Keywords

  • meta learning
  • neural contextual bandits
  • recommendation
  • user modeling

ASJC Scopus subject areas

  • Software
  • Information Systems

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