# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 1 Introduction
However, employing a single similarity metric is inadequate in fully capturing the intricate complexity and significance of inter-session dependencies. To solve the above problems, we propose an SBR model that **D**iscretly **E**xploits **I**nter-**S**ession **I**nformation (DEISI). **For the first problem**, as factor-level label data are usually not available in the SBR datasets, obtaining factor-level information is not feasible. Therefore, we propose the method of Disentangled Representation Learning (DRL), seeking to generate independent factor-level embeddings that reveal and disentangle the latent factors that are present in the observed data. By leveraging factor-level session embeddings, different factor-level inter-session dependencies can be established on corresponding factors. **For the second problem**, we tend to assess the necessity of inter-session information for current recommendations before incorporating them, for example, in factor-level analysis (e.g., brand and color), if it is observed from interactions that the user consistently chooses a particular brand but selects various colors. This suggests that it is not the brand but the color that poses a challenge to the user's decision-making process. Hence, solely considering the user's consistent interest in the brand associated with intra-session information is adequate for brand recommendation. However, for color selection recommendations, additional inter-session information may be necessary to facilitate models in uncovering the underlying preferences. In essence, observed stable factors require minimal inter-session information, while the complexities associated with selections can benefit from the incorporation of inter-session information to unravel the evolving user preferences. Based on this consideration, we propose a novel metric called'stability', which is used together with the original similarity to measure the inter-session dependency. In addition, we adopt contrastive learning (CL) [20, 21, 22] to improve the robustness of the model, considering that data sparsity in SBR often leads to inaccurate embedding learning. To summarize, the main contributions of this paper are as follows:  
* We introduce DRL to refine the inter-session dependencies of SBR to the factor level to detail the different strengths of factor-level dependencies. We measure the inter-session dependencies from a new perspective to propose a new metric called'stability' as a supplement to the original similarity metric to more comprehensively explain inter-session dependencies. * Building upon the above enhancements, we propose a strategy to **D**iscretely **E**ploit **I**nter-**S**ession **I**nformation (DEISI), which forms the basis of our proposed model DEISI-GNN. Additionally, we incorporate contrastive learning to improve the model's ability to learn embeddings. * Extensive experiments on three datasets were conducted to demonstrate the superior performance of our proposed DEISI model compared with the state-of-the-art models. The remaining sections of this article are organized as follows: Section 2 provides an overview of related studies. Section 3 utilizes formulas to present fundamental information pertaining to SBR. Section 4 elaborates on the overarching framework and specific details of DEISI-GNN. Section 5 introduces comprehensive experiments to demonstrate the superiority of DEISI-GNN. Section 6 presents a summary of the paper.