SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning

TL;DR

SR-GCL introduces a contrastive learning framework with global context augmentation for session-based recommendations, effectively addressing data sparsity and noise, and demonstrating superior performance on real-world datasets.

Contribution

The paper proposes a novel contrastive learning approach with global context augmentation methods specifically designed for session-based recommendation systems.

Findings

Outperforms state-of-the-art methods on E-commerce datasets

Effective in handling data sparsity and noisy interactions

Enhances session representation with global context augmentation

Abstract

Session-based recommendations aim to predict the next behavior of users based on ongoing sessions. The previous works have been modeling the session as a variable-length of a sequence of items and learning the representation of both individual items and the aggregated session. Recent research has applied graph neural networks with an attention mechanism to capture complicated item transitions and dependencies by modeling the sessions into graph-structured data. However, they still face fundamental challenges in terms of data and learning methodology such as sparse supervision signals and noisy interactions in sessions, leading to sub-optimal performance. In this paper, we propose SR-GCL, a novel contrastive learning framework for a session-based recommendation. As a crucial component of contrastive learning, we propose two global context enhanced data augmentation methods while maintaining the semantics of the original session. The extensive experiment results on two real-world E-commerce datasets demonstrate the superiority of SR-GCL as compared to other state-of-the-art methods.