Graph Neural Bandits

Yunzhe Qi, Yikun Ban, Jingrui He

Keywords: Contextual Bandits, Graph Neural Networks

Abstract: Contextual bandits aim to choose the optimal arm with the highest reward out of a set of candidates based on their contextual information, and various bandit algorithms have been applied to personalized recommendation due to their ability of solving 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, we model the "fine-grained'' collaborative effects through estimated user graphs in terms of exploitation and exploration individually. 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.

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