GNN-based Reinforcement Learning Agent for Session-based Recommendation

Mohammadi Zaki, Brijraj Singh, Pankaj Wasnik

Keywords: Graph Neural Network, Reinforcement Learning, Recommendation Systems

Abstract: This paper focuses on session-based item recommendation and the challenges of using Reinforcement Learning (RL) in recommender systems. While traditional RL methods rely on one-hot encoded vectors as user state, they often fail to capture user-specific characteristics, which may provide misleading results. In contrast, recently, Graph Neural Networks (GNNs) have emerged as a promising technique for learning user-item representations effectively. However, GNNs prioritize static rating prediction, which does not fully capture the dynamic nature of session-based recommendations. To address these limitations, we propose a novel approach called GNN-RL-based Recommender System (GRRS), which combines both frameworks to provide a unique solution for the session-based recommendation \footnote{Code available at \url{https://anonymous.4open.science/r/iclr24_gnn_rl/}}. We demonstrate that our method can leverage the strengths of both GNNs and RL while overcoming their respective shortcomings. Our experiments on several logged public datasets validate the efficacy of our approach over various SOTA algorithms. Additionally, we offer a solution to the \emph{offline training problem}, which is often encountered by RL algorithms when employed on logged datasets, which may be of independent interest.

Primary Area: reinforcement learning

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Submission Number: 3330