## 1 Introduction  
Session-based recommendation is a typical application of recommender systems. The initial exploration of session recommendation systems mainly focused on sequence modeling, which mainly includes project-based neighborhood methods [1] and Markov-chain-based sequence methods [2]. Although the project-based approach completes recommendations based on an item similarity matrix, it ignores the order between items. To solve this problem, the Markov-chain-based approach uses the sequential behavior between two adjacent clicks to model a user's short-term preferences [3], but it cannot capture long-term preferences. [PERSON] et al. [4] combined the two to avoid the dependence on local sequence information. Deep-learning-based models mainly include recurrent neural networks (RNNs) and their variants, attention mechanisms, and graph neural networks. [PERSON] et al. [5] proposed gated recurrent unit for recommendation (GRU4 Rec), and used recurrent neural networks (RNNs) to model sessions based on clicks for the first time. [PERSON] et al. [6] proposed a rich feature-oriented session recommendation model based on recurrent neural networks, a parallel RNN model (P-RNN), aiming at the deficiencies of GRU4 Rec, which only models project identification and fails to make full use of rich project features (pictures, text, etc). [PERSON] et al. [7] proposed applying residence time (the interaction interval between users and items) to the RNN-based session recommendation method, and proposed a Dewell time-based RNN, which takes the interaction time between the user and the project as the key feature for extracting the user's interest preference, suitable for the current popular field of short video recommendation. To solve the same problem, [PERSON] et al. [8] proposed a new model structure, a session recommendation model (TA4 Rec) based on recurrent neural networks with a temporal attention factor. TA4 Rec calculates the attention factor based on the user's residence time on the project and adds this to a gated recurrent unit (GRU) network to improve model performance. [PERSON] et al. [9] proposed the II-RNN (Inter-Intra RNN) model, which combines modeling within a single session with modeling between historical sessions, to solve the problem that users' interest preferences cannot be obtained due to the absence of an item sequence at the beginning of each session. [PERSON] et al. [10] proposed a hierarchical recurrent neural network personalized session recommendation method HRNN (hierarchical recurrent neural), aiming at existing models that only consider item identification and characteristics and ignore user identification networks, and adding user-level GRUs to GRU4 Rec. [PERSON] et al. [11] proposed to learn the embedded representation of users and projects from the interaction data between users and projects, combine the global user embedding with the project sequence in the session, and generate the next recommendation through an attentive GRU (AGRU)-type structure. [PERSON] et al. [12] used the context preferences of higher-level users to expand the RN-based session recommendation method, and proposed an augmented RNN (ARNN), whose core structure is a product-based neural network [13] (PNN) that can capture the high-level interactions between the user context and the project to simulate the user's context preferences, and can enhance the effect of the existing RNN model. However, the user preferences contained in the sequence are also ignored, and the information is not fully utilized. Then, the RNN model is combined with an attention mechanism, and the accuracy of the model prediction is further improved. The NARM (neural attentive recommendation machine) model was designed as an encoder that blends an RNN and attention mechanism to model the sequential behavior of users [14]. Session-based recommendation with graph neural network (SR-GNN) [15] was the first model to apply graph neural networks to session-based recommendation methods.