# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 1 Introduction  
Traditionally, recommendation models rely on historical interactions to capture users' long-term interests. However, in some cases, such as when dealing with new users or in scenarios with privacy concerns, it may not be possible to access historical data. In contrast, session-based recommendation (SBR) focuses on making predictions based solely on an anonymous current session, without relying on any historical data. Furthermore, the current landscapes for recommendation systems to emphasize short-term user interests aligns perfectly with the strengths of session-based recommendation approaches. Based on the two main aforementioned advantages, SBR has become a popular and important research topic in recent years. From a sequence processing perspective, many commonly used natural language processing (NLP) techniques are applicable to SBR, such as Recurrent Neural Network(RNN) [1; 2; 3], attention mechanism [4; 5], and Graph Neural Network (GNN) [6; 7; 8]. Due to better complex relationship modeling capabilities, GNN-based SBR models have become the current mainstream. Early GNN-based SBR models [9; 10; 11; 12; 13] only consider intra-session information for the next-item recommendation. For example, session-based recommendation with Graph Neural Networks (SR-GNNs) [12] model separated session sequences into graph structured data and use Graph Neural Networks to capture item transitions. A Target Attentive Graph Neural Network (TAGNN) [13] uses a target-aware attentive network to generate the session embedding. A Disentangled Graph Neural Network (Disen-GNN) [14] decomposes item embeddings into distinct factor-specific embeddings through the application of disentangled representation learning. Subsequently, it leverages a Gated Graph Neural Network (GGNN) architecture to learn factor-wise representations by operating on factor-specific item adjacency matrices, where each matrix encodes the pairwise item similarities within its corresponding factor space. However, these models limit their performance because one session carries inadequate information for effective learning. To improve the performance, some models include additional information from other sessions [15; 16; 17; 18; 19]. A Full Graph Neural Network with Broadly Connected Session (FGNN-BCS) [15] comprehensively models both intra- and inter-session dependencies, and proposes a Full Graph Neural Network (FGNN) that operates on session-level graphs. A CO-Training framework for session-based RECommendation (COTREC) [17] proposes a dual-view contrastive learning framework that leverages both internal and external session connectivities through two specialized graph encoders, which generate mutually supervised training signals by recursively exploiting complementary connectivity patterns between sessions. These models build an inter-session graph to capture dependencies between sessions and utilize graph convolution techniques to effectively transfer inter-session information. Nevertheless, the acquired information from other sessions may consist of both relevant and interfering components. Existing models have demonstrated limitations in accurately capturing pertinent information while effectively filtering out extraneous interference, mainly due to the imperfect establishment of inter-session dependencies. **The first problem** stems from the fact that inter-session relationships are commonly not disaggregated at the factor level, despite the existence of varying degrees of dependency between sessions and different factors. For example, when comparing two clothing purchase sessions with similar brand preferences but distinct color preferences, it is advisable to establish a strong association for the former factor and a weak association for the latter factor. However, existing models uniformly assign a moderate level of inter-session dependency at the item level, which lacks the necessary precision. **The second problem** lies in the prevalent practice of relying solely on similarity as the metric for measuring inter-session dependencies.