Graph Spring Network and Informative Anchor Selection for session-based recommendation

Abstract

Session-based recommendation (SBR) aims at predicting the next item for an ongoing anonymous session. The major challenge of SBR is how to capture richer relations in between items and learn ID-based item embeddings to capture such relations. Recent studies propose to first construct an item graph from sessions and employ a Graph Neural Network (GNN) to encode item embedding from the graph. Although such graph-based approaches have achieved performance improvements, their GNNs are not suitable for ID-based embedding learning for the SBR task. In this paper, we argue that the objective of such ID-based embedding learning is to capture a kind of neighborhood affinity in that the embedding of a node is similar to that of its neighbors’ in the embedding space. We propose a new graph neural network, called Graph Spring Network (GSN), for learning ID-based item embedding on an item graph to optimize neighborhood affinity in the embedding space. Furthermore, we argue that even stacking multiple GNN layers may not be enough to encode potential relations for two item nodes far-apart in a graph. In this paper, we propose a strategy that first selects some informative item anchors and then encode items’ potential relations to such anchors. In summary, we propose a GSN-IAS model (Graph Spring Network and Informative Anchor Selection) for the SBR task. We first construct an item graph to describe items’ co-occurrences in all sessions. We design the GSN for ID-based item embedding learning and propose an item entropy measure to select informative anchors. We then design an unsupervised learning mechanism to encode items’ relations to anchors. We next employ a shared gated recurrent unit (GRU) network to learn two session representations and make two next item predictions. Finally, we design an adaptive decision fusion strategy to fuse two predictions to make the final recommendation. Extensive experiments on three public datasets demonstrate the superiority of our GSN-IAS model over the state-of-the-art models.