## 4 Experiments and Analysis of Results
### Model Performance Comparison and Analysis  
To demonstrate the validity of the MGMI-CEHCN model, we compared it with the following methods: (1) S-POP recommends by giving priority to the items that are most frequently observed during the present interaction. (2) SKNN determines the relevance of candidate items by analyzing their frequency of occurrence in neighboring sessions. (3) GRU4 Rec [5] learns and captures patterns and dependencies in sequential data by recursively learning and capturing them, in order to better understand and predict associations and transitions between different elements in a session. (4) NARM [14] uses an attention mechanism to capture the user's main purpose. (5) SR-GNN [15] applies graph neural networks to session data to exploit interaction relationships in graph structures. (6) LESSR [33] successfully solves the problem of information loss in graph neural network-based SBR models. (7) S2-DHCN [17] constructs hypergraphs to capture higher-order dependencies between items and mine complex relationships between items. (8) COHHN [46] constructs heterogeneous hypergraph networks to represent information and enhances the relationship between price and interest preferences through co-leading patterns. The experimental results for the overall performance are shown in Table 2, with the best results in each column highlighted by underlining. The following conclusions can be drawn from the analysis:  
As can be seen in Table 2, the RNN-based recommendation methods outperformed traditional methods such as S-POP and SKNN, which confirms their key role in session recommendation modeling. NARM outperformed GRU4 Rec to a large extent, which can be attributed to the attentional mechanism introduced to capture the main intention in the session. The MGMI-CEHCN and GNN-based models (SR-GNN LESSR) and S2-DHCN outperformed traditional approaches because they take into account the sequential information embedded in the session data and incorporate the user's long- and short-term interests to capture the dynamic variability of user interests and thus achieve efficient recommendation performance. The results also showed that session preference encoders augmented with graph neural networks show remarkable results in learning more accurate session representations from session sequences. The SR-GNN model, which also uses graph neural networks and attentional mechanisms, was not as capable in extracting session-level representations as the MGMI-CEHCN model, which combines contrast enhancement strategies. This further confirms the importance of filtering out irrelevant user behavioral data for the MGMI-CEHCN, where the adjunctive task based on contrast learning and the goal-supervised task constitute a complementary modeling mechanism that can address the data relevance problem inherent in the representation of implicit feedback and weakly-supervised problems, in principle, by learning robust representations that are applicable to session-based recommendations. The gnn-based approach (SR-GNN) relies on the ability to model pairwise relationships between nodes to achieve competitive performance. S2-DHCN models higher-order relationships between items. In addition, COHHN models users' price preferences and interest preferences by extracting them through the attention layer, and its performance is greatly improved. However, fundamentally, these methods can only parse out a single level of information, resulting in a model that cannot accurately capture the multiple potential interests hidden in various item features. As a result, their performance was still inferior to the proposed MGMI-CEHCN model. The MGMI-CEHCN model proposed in this paper showed a large advantage over all datasets. On Cosmetics, its P@20 reached up to 55.25% and M@20 reached up to 38.26%, an improvement of 3.06% and 3.09%, respectively; on Diginetica-buy, P@20 reached up to 65.60% and M@20 reached up to 27.47%, an improvement of 2.47% and 6.64%, respectively.