HybridGNN-SR: Combining Unsupervised and Supervised Graph Learning for Session-based Recommendation

Abstract

Session-based recommendation aims to predict the next item that a user may visit in the current session. By constructing a session graph, Graph Neural Networks (GNNs) are employed to capture the connectivity among items in the session graph for recommendation. The existing session-based recommendation methods with GNNs usually formulate the recommendation problem as the classification problem, and then use a specific uniform loss to learn session graph representations. Such supervised learning methods only consider the classification loss, which is insufficient to capture the node features from graph structured data. As unsupervised graph learning methods emphasize the graph structure, this paper proposes the HybridGNN-SR model to combine the unsupervised and supervised graph learning to represent the item transition pattern in a session from the view of graph. Specifically, in the part of unsupervised learning, we propose to combine Variational Graph Auto-Encoder (VGAE) with Mutual Information to represent nodes in a session graph; in the part of supervised learning, we employ a routing algorithm to extract higher conceptual features of a session for recommendation, which takes dependencies among items in the session into consideration. Through extensive experiments on three public datasets, we demonstrate that HybridGNN-SR outperforms a number of state-of-the-art methods on session-based recommendation by integrating the strengths of the unsupervised and supervised graph learning methods.