Hyperbolic spatial-temporal network for session-based recommendation

Abstract

The goal of Session-based recommendation is to recommend the next item a user may be interested in based on historical click data, without relying on user profiles. Compared to the data from other recommendation scenarios, session data is typically more sparse, thus Self-supervised learning (SSL), which derives ground-truth from raw data, has gradually gained attention. Existing SSL methods usually augment data by dropping or transforming original sequences in Euclidean space, leading to two problems. Firstly, in Euclidean space, it is difficult to handle the distorted distribution and hierarchical nature of session data, and secondly, the essential spatial and temporal features of session data are usually overlooked. To address these issues, we propose the Hyperbolic Spatial-Temporal Network (HSTN), which enhances the performance by generating positive samples with the spatial-temporal features of session data in hyperbolic space. Specifically, we first use a hyperbolic hypergraph neural network as the base encoder to mitigate the influence of distorted data distribution. Then, we employ a spatial-temporal feature learning module to generate positive samples with spatial-temporal features for contrastive learning. Extensive experiments on three real-world datasets demonstrate that our proposed method achieves improvements of 11.6 %, 17.6 %, and 38.4 %, respectively, compared to current state-of-the-art methods, under P@10 metric.