## 2 The Proposed Model Algorithm  
### Model Structure Design  
In the session recommendation task, different items are associated with each other due to various diverse and complex factors, and recommending to the current session user requires considering these complex associations between items. This paper constructs session data as a session graph, global graph, and feature graph, and models the historical session data from two perspectives of session and global neighborhood to obtain user preferences, which not only realizes the information transmission between different sessions, but also further deeply mines the hidden information in the global domain. This model also integrates the item feature information, fully mining the user preferences hidden in the item feature information. The overall architecture of the model is shown in Figure 1 below. The input data in the model are all anonymous behavior sequences, and session recommendation predicts the next possible item of interest to the user based on these anonymous behavior sequences, thus generating effective recommendations. The session sequence data first need to be initialized after embedding, forming the initial embedding representation of the item, and then using the constructed global graph, session graph, feature graph, and using the powerful graph data learning and representation ability of graph neural networks, learning to capture the high-order embedding representation of the item. Among them, the item feature graph is based on the attributes and context feature information of the item itself to characterize the item, which implies the user's interest preference. The model mainly consists of four modules: the item representation learning module at the session layer, the item representation learning module at the global domain layer, the aggregation module and the prediction scoring module. 1. The item representation learning module of the conversation layer. Based on the conversation sequence data, a conversation graph is constructed, and a GNN network model is used to learn the conversation layer item embedding representation in the current conversation. In the process of constructing the item connection matrix of the conversation graph, the interaction temporal information is considered, and the interaction temporal information is encoded to obtain the initial vector of the conversation node; secondly, in the item node learning representation of the conversation graph, the item feature information  
Figure 1: Structure diagram of the proposed model. of the conversation itself is fully utilized, the domain set node information representation of the item is combined with its own feature information, and the item preference hidden in the item feature information is deeply mined by using the graph neural network. 2. The item representation learning module of the global domain layer. Based on all the conversation sequence data, a weighted global graph is constructed, which is a weighted graph that collects all the conversation sequence information. In the process of calculating the edge weights in the global graph, not only the frequency of each edge in all the conversations is considered, but also different weights are assigned to them according to the different \"gaps\" of the items in the conversation sequence. Then, by using an attention mechanism with a session-awareness function, based on the global graph structure, the embedding representation of the neighbor set of each node is aggregated to learn the global domain-level item embedding representation on all the conversations. Finally, using the graph neural network to deeply learn the item preference hidden in the item feature information by fusing the embedding representation vector of the item feature information and the embedding representation vector of the neighbor set, the item representation vector of the global domain layer is obtained. 3. The aggregation module.