## 1 Introduction
SR-GNN constructs a sequence of sessions as a graph structure containing only one type of node, and uses a GNN to capture the complex transition relationships between items and to generate a vector of potential item representations with better expressive power. With the successful application of the SR-GNN model, some variants of GNNs have also achieved good performance. Among them, in the rethinking the item order in session-based recommendation with graph neural networks (FGNN) model proposed by [PERSON] et al. [16], the SR-GNN is compensated for by considering transformation modes for specific items within the session. Although the above methods have achieved good performance in SBR, they still fail to capture complex high-level relationships between projects. The use of hypergraphs can capture higher-order item relationships to a certain extent [17]. A hypergraph neural network (HGNN) is used to learn the embedded representation of items to mine higher-order information. [PERSON] et al. [18] constructed a set of conversations as a hypergraph and used a dual-channel hypergraph convolutional network (DHCN). The DHCN constructs hypergraphs to capture higher-order dependencies between items, and is able to learn complex transformational relationships between items, thus improving the accuracy of recommendations. Although the above methods have achieved good performance, most of them focus on the complex structure and transformation relationships between items or higher-order dependencies between multiple items, neglecting the importance of the granular information of item price and category. STAMP (short-term attention/memory priority) utilizes an MLP network and attention mechanism to capture users' general and current interests [19]. [PERSON] et al. [20] first introduced time interval information into a session-based recommendation method and proposed a time-aware graph neural network (TA-GNN), which takes the time factor as a key feature for user interest preference learning. These methods only modeled the user's primary interests in a session and ignored the secondary interests. However, in general, for each session, each item reflects the user's unique interests. If two items share similar interests, they are usually considered relevant; on the contrary, if their interests are different, they may be less relevant to each other. Items containing multiple potential interests in each session are modeled to more accurately capture the correlation between items. In addition, the problem of sample sparsity still hinders the modeling of user-item interactions. In recent years, comparative learning has been gradually promoted as an emerging class of methods to address data sparsity in session recommendation [18; 21; 22]. A multi-granularity and multi-interest contrast-enhanced hypergraph convolutional network model (MGMI-CEHCN) for session recommendation is proposed. The main contributions of this work are as follows:  
* A heterogeneous hypergraph-based approach is proposed to embed the category and price information of an item as auxiliary information, enhance the item representation with multi-granularity information, and then better capture the complex dependencies between nodes through a hypergraph convolutional network. * In MGMI-CEHCN, an interest perceptron is used to detect multiple potential interests for each item, a decentralized interest extraction network is used to integrate the user's final interests, and a global session representation is obtained through a soft attention mechanism. * To alleviate the sparsity of the data, the global session representation and the local session representation are jointly compared, and supervised signals are built for both, to optimize the training, achieve more effective mining of deeper associations between items, and increase the model robustness and interpretability. The remainder of this paper is structured as follows: In Section 2, we present a literature review of session-based recommendation research.