Graph Neural Bandits

Abstract

Contextual bandits algorithms aim to choose the optimal arm with the highest reward out of a set of candidates based on the contextual information. Various bandit algorithms have been applied to real-world applications due to their ability of tackling the exploitation-exploration dilemma. Motivated by online recommendation scenarios, in this paper, we propose a framework named Graph Neural Bandits (GNB) to leverage the collaborative nature among users empowered by graph neural networks (GNNs). Instead of estimating rigid user clusters as in existing works, we model the "fine-grained" collaborative effects through estimated user graphs in terms of exploitation and exploration respectively. Then, to refine the recommendation strategy, we utilize separate GNN-based models on estimated user graphs for exploitation and adaptive exploration. Theoretical analysis and experimental results on multiple real data sets in comparison with state-of-the-art baselines are provided to demonstrate the effectiveness of our proposed framework.

arXiv commentAccepted to SIGKDD 2023

References in corpus (21)

• Neural Graph Collaborative Filtering
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• A Contextual-Bandit Approach to Personalized News Article Recommendation
• Neural Tangent Kernel: Convergence and Generalization in Neural Networks
• Simplifying Graph Convolutional Networks
• Representation Learning on Graphs with Jumping Knowledge Networks
• A Convergence Theory for Deep Learning via Over-Parameterization
• Predict then Propagate: Graph Neural Networks meet Personalized PageRank
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
• Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
• Finite-Time Analysis of Kernelised Contextual Bandits
• Online Clustering of Bandits
• Neural Contextual Bandits with UCB-based Exploration
• Neural Thompson Sampling
• Multi-Task Learning for Contextual Bandits
• Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
• EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
• Neural Bandit with Arm Group Graph
• Multi-facet Contextual Bandits: A Neural Network Perspective
• Neural Collaborative Filtering Bandits via Meta Learning
• Graph Neural Network Bandits

Cited by in corpus (3)

• ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration
• Treatment Effect Estimation for User Interest Exploration on Recommender Systems
• Leveraging heterogeneous spillover in maximizing contextual bandit rewards