## 1 Introduction  
In recent times, mainly due to the impact of the pandemic, there has been a dramatic increase in individuals' use of the Internet for information searches and shopping. This trend has led to a significant rise in the volume and variety of product offerings on e-commerce platforms, driving the rapid proliferation of recommendation systems designed to enhance users' shopping experiences and present more attractive products [1]. Recommendation systems have become critical across various industries in the era of information abundance, aiming to reduce information overload for users [2, 3]. Typically, these systems provide recommendations based on data from three primary sources: products, user characteristics, and current status [4, 5]. However, the lack of profile information for new users hampers the effectiveness of recommendation systems. To address this drawback, session-based recommendation (SBR) systems, which rely on short-term interactions with anonymous users, have gained prominence. SBR has become an increasingly popular research topic and the focus of many scholars [6; 7]. The basic principle of SBR is to leverage short interaction sequences of anonymous users, analyze the conversion patterns within these sequences, and predict the next most likely item to be clicked. Early implementations of SBR used a neighborhood-based model, providing recommendations based on statistical similarities between items within the current session. With increasingly stringent information laws, users are becoming more concerned about the privacy of their personal data, making it difficult to collect user characteristics and past purchase data. This challenge highlights the importance of session-based recommendation (SBR) systems, which aim to understand and predict users' preferences based on their most recent interactions. SBR systems operate by analyzing a specific set of user interaction records. Given the dynamic changes over time and across different contexts, effectively modeling the limited behaviors observed during a session and predicting the user's following likely action has become crucial. Solving the session-based recommendation problem requires significant effort [8; 9; 10]. Initially, many recommendation methodologies utilize interaction-based timestamps to rank items, focusing on sequence patterns to predict the next item of interest. A simple Markov chain-based approach aims to predict the user's next click based on previous behavior using the independence assumption, which has limitations in complex interaction scenarios [11; 12]. Subsequently, deep learning-based recommendation systems, including recurrent neural network (RNN) methods, which can model longer-term historical behavior based on contextual information, have become mainstream [13; 14; 15]. However, effectively modeling complex needs within limited user behaviors remains a challenge. In SBRs, RNN-based approaches struggle to determine item relationships and accurately assess user preferences due to limited user interaction [16]. Additionally, graph neural network (GNN) methods focus on investigating relationships between items but do not account for the user's time-varying behavioral patterns during the discovery process [17; 18; 19]. These methods often break down the sequential structure of the session, concluding that priority depends on the last item in the current session. Consequently, they fail to represent the temporal dynamics in recommender systems, reducing their effectiveness. Thus, the order of clicks and the time intervals between clicks throughout a user session must be considered. Several key factors need to be considered to better understand user behavior in session-based recommendation systems. First, sessions with the same order but different time intervals can be detected as similar behavioral patterns by RNN-based methods. High click activity occurring at shorter intervals may indicate intense user interest in particular content.