# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 6 Conclusions  
In this paper, we proposed DEISI-GNN, a novel GNN-based model for session-based recommendation that effectively leverages inter-session information while mitigating the interference caused by irrelevant data. Traditional SBR models often struggle with the limited information available within a single session, leading to suboptimal recommendation performance. While incorporating inter-session information has shown promise in enhancing recommendation accuracy, existing approaches frequently fail to distinguish between useful and irrelevant inter-session data, resulting in noise that degrades model performance. To address the above-mentioned challenges, DEISI-GNN introduces two key components: factor-level inter-session dependency learning and a novel stability metric for assessing inter-session relationships. By employing disentangled representation learning, DEISI-GNN refines inter-session dependencies at the factor level, allowing for accurate modeling of user preferences across different latent factors. This approach enables the model to capture finer-grained interactions between sessions, which is particularly beneficial in scenarios where user preferences vary across different factors (e.g., brand vs. color in clothing purchases). Additionally, the stability metric complements the traditional similarity measure by evaluating the consistency of user interests within a session. This metric helps the model determine when inter-session information is necessary and when it may introduce noise. By dynamically adjusting the weights of inter-session dependencies based  
Figure 6: Performance of I3 GN and I3 GN-W on Nowplaying. Figure 7: Performance of COTREC and COTREC-W on Diginetica. on stability, DEISI-GNN ensures that only relevant inter-session information is utilized, thereby improving the robustness and accuracy of recommendations. To further enhance the model's performance, we incorporated contrastive learning into DEISI-GNN. Contrastive learning helps the model learn more robust embeddings by comparing data from the intra-session and inter-session modules, which is particularly effective in data-sparse environments. This self-supervised learning approach ensures that the model can effectively distinguish between essential and non-essential information, leading to more stable and accurate recommendations. Extensive experiments conducted on three benchmark datasets--Nowplaying, Yoo-chose1/64, and Diginetica--demonstrated the superior performance of DEISI-GNN compared with state-of-the-art baselines. The results showed significant improvements in recommendation accuracy with the DEISI-GNN model. In conclusion, DEISI-GNN represents a significant advancement in session-based recommendation by addressing the limitations of existing models in handling inter-session information. By refining inter-session dependencies at the factor level and introducing a stability metric to filter out irrelevant data, DEISI-GNN achieves more accurate and robust recommendations. The incorporation of contrastive learning further enhances the model's ability to learn embeddings, even in data-sparse environments. Future work could explore the application of DEISI-GNN in other recommendation scenarios, such as multi-intent recommendations, and investigate the potential for further improvements through advanced disentanglement techniques or alternative stability measures. Overall, DEISI-GNN provides a promising framework for leveraging inter-session information in a controlled and effective manner, paving the way for more sophisticated and accurate session-based recommendation systems. Conceptualization, J.S. and G.W. ; methodology, J.S. ; software, Z.W. ; validation, J.S., Z.W., and H.W. ; investigation, Z.W. ; resources, G.W. ; data curation, Z.W. ; writing--original draft preparation, J.S. ; writing--review and editing, G.W. ; visualization, H.W. ; supervision, G.W., B.Q. and D.H. All authors have read and agreed to the published version of the manuscript.