## 5 Experimental Evaluation
### Performance Comparison  
#### 5.5.1 Experiment 1  
In this section, we present the experimental results of our comparative analysis of different parameter configurations. We conducted a comprehensive evaluation of the proposed APGCN-CF model against baseline methods across two distinct datasets, focusing on three key performance metrics: accuracy, recall, and F1-scores. Figure 3 presents a detailed comparative analysis of the recommendation performance, illustrating how APGCN-CF performs relative to the baseline methods across all two datasets in terms of these critical evaluation metrics.  
Our comprehensive experimental analysis reveals the superior performance of APGCN-CF compared to baseline approaches across multiple evaluation metrics. The performance comparison results can be analyzed from the following aspects:  
First, compared to graph-based baseline NGCF, APGCN-CF demonstrates consistent superiority across top-5 and top-10 metrics on both datasets. While NGCF leverages graph neural architectures, its simple message-passing mechanism fails to capture the complex spatio-temporal dependencies in minor agricultural product recommendation scenarios. Specifically, NGCF's embedding propagation layer shows limited capability in modeling seasonal and regional characteristics, resulting in substantially lower F1-scores on both garlic and scallion datasets.  
Second, traditional matrix factorization-based methods (MF and SVD) exhibit inherent limitations in our experimental scenarios. MF's inability to model high-order feature representations leads to suboptimal recall performance, particularly in top-5 and top-10 recommendations. Although SVD shows marginal improvements over MF in top-10 and top-20 scenarios through linear factorization, it still underperforms compared to graph-based approaches due to its limitations in modeling non-linear relationships and dynamic user preferences.  
Third, conventional collaborative filtering approaches (item-CF and user-CF) demonstrate significant performance gaps. User-CF achieves notably low F1-scores (0.031 and 0.045 for garlic and scallion, respectively, in the top-5 scenario), primarily due to the high sparsity of user-item interactions impeding similar user identification. Item-CF's elementary similarity calculation mechanism proves insufficient for capturing critical seasonal and regional patterns in minor agricultural product recommendations.  
In contrast, APGCN-CF achieves superior performance through its sophisticated architecture for high-order feature extraction and dynamic recommendation generation. Quantitative results show that APGCN-CF attains remarkable recall scores (0.146 and 0.193 for garlic and 0.140 and 0.234 for scallion for the top-5 and top-10 scenarios, respectively), consistently outperforming all baselines. The integration of temporal and spatial dimensions enables APGCN-CF to effectively model seasonal variations and geographical attributes, thereby generating highly personalized recommendations aligned with users' contextual preferences. This comprehensive modeling capability is particularly evident in top-20 metrics, where APGCN-CF demonstrates significant advantages in both precision and recall, validating its effectiveness in minor agricultural product recommendation scenarios.