Noise-Resistant Graph Neural Networks for Session-Based Recommendation

Session-based recommendation have received increasing attention due to the importance of privacy and user data protection, aiming to predict the next click of a user based on a short anonymous interaction sequence. Previous works have focused on users’ long-term and short-term preferences, ignoring the noise problem in session sequences. However, session data is inevitably noisy, as it may contain incorrect clicks that are inconsistent with the user’s true intent due to misleading product information. Therefore, in this paper, we propose a novel framework called Noise-Resistant Graph Neural Networks (NRGNN) to address the noise problem in session-based recommendation. NRGNN innovatively introduces two key components: Noise-Resistant Graph Contrastive Learning (NR-GCL) and Cross-Session Enhanced Short Preference (CS-SP). NR-GCL is a graph contrastive learning method that employs minor perturbation augmentation to reduce the impact of noise problem in the entire session on the accuracy of the results. CS-SP utilizes cross-session information, aiming to address the problem of poor recommendation accuracy when the last item is noisy. To evaluate our proposed method, we conduct comprehensive experiments on three real-world datasets. The experimental results demonstrate that NRGNN outperforms the state-of-the-art methods.