## 1 Introduction
### Related Work  
Traditional session recommendation methods usually use data mining or machine learning techniques to mine the relevance within the session data, so as to carry out session recommendation [6]. For example, this kind of recommendation method based on frequent patterns and association rules first performs data mining of frequent patterns and association rules [7], then performs session matching and, finally, generates recommendation results. There are also session recommendation algorithms based on sequence pattern mining, such as using weighted sequence pattern mining related to users for recommendation, where the weight of each session sequence is calculated by its similarity with the historical interaction sequence of the target user [8]. There are also hybrid recommendation models that combine sequence pattern mining and collaborative filtering methods, so as to model the user's dynamic short-term preferences and long-term preferences [9; 10]. Another effective session recommendation method is the session recommendation method based on a Markov chain, which maps the current session to a Markov chain, and then predicts the next most likely interaction behavior of the user based on the previous behavior [11]. However, the model generally uses the last interaction record in the session sequence to predict the user's next behavior, only using the local features in the session, and ignoring the role of context information in the session data. Compared with traditional methods, deep learning can more effectively extract data features. Based on deep learning, recommendation algorithms train user interest preference representation through neural network models and use it to predict the next interaction [12; 13; 14]. For example, taking advantage of a recurrent neural network RNN in processing serialized data, [PERSON] proposed the GRU4 Rec model based on GRU (gated recurrent unit) [15], which only uses the user's historical interaction data in the session for recommendation, and performs feature representation on the item sequence that the user clicked or browsed in a session. [PERSON] proposed a model using multi-layer recurrent neural networks [16], which considered the connection between different sessions, and used the feature representation of the previous session as the input of the next session modeling network. [PERSON] ] proposed the NARM model [17], which integrated the ideas of an RNN model and attention mechanism and focused on both the user's sequential behavior and interest preference. [PERSON] and [PERSON] proposed the HiCAR model [18], which divided the context information in the session into macro-level and micro-level contexts and used a multi-way hybrid strategy learning module to model the multi-feature interaction. [PERSON] proposed the STAMP (Short-TermAttention/Memory Priority) model [19], which obtained the user's long-term interest from the long-term memory of the session context and represented the user's current interest by the last interacted item, effectively capturing the user's interest preference information; however, this method did not consider the behavior information of the session sequence. BACH used convolutional operations to extract complex local features and iteratively RNN models to extract the user's long-term interest from the session sequence [20,21]. However, these model algorithms can only model the one-way transition relationship between items in the session and cannot deeply analyze and mine the transition relationship between other items in the session, which seriously affects the further effective improvement of the recommendation accuracy. In recent years, due to the natural advantages of graph neural networks (GNNs) in solving graph structured data problems and their interpretability characteristics [22, 23, 24], they can naturally and explicitly encode the crucial collaborative signal to improve the user and item representations.