## 5 Experimental Evaluation
### Discussion  
The comprehensive experimental evaluation of APGCN-CF demonstrates its significant advantages over existing state-of-the-art methods in minor agricultural product recommendation scenarios. The superior performance can be attributed to several key factors: first, the integration of graph convolutional networks substantially enhances the model's capability to capture high-order connectivity patterns in user-item interactions, as evidenced by the consistent improvements observed in ablation studies comparing APGCN-CF with APGCN-CF-B. The graph convolution operations effectively model complex relationships between users and agricultural products, leading to more accurate preference predictions. Second, our framework successfully addresses data sparsity challenges through neighborhood information aggregation mechanisms that leverage the structural properties of user-item interaction graphs. This is particularly valuable in minor agricultural product recommendation contexts where user-item interactions are typically limited. Finally, the proposed approach effectively incorporates spatio-temporal characteristics into the recommendation process, capturing the seasonal and regional dependencies inherent in agricultural product consumption patterns. This is reflected in the improved recommendation accuracy across both garlic and scallion datasets, where temporal and geographical factors significantly influence purchasing behaviors.  
Our third experiment further validates the importance of spatio-temporal characteristics in the recommendation process. By systematically removing temporal features (APGCN-CF-C), geographical features (APGCN-CF-D), or both (APGCN-CF-E), we quantified the contribution of these components to recommendation performance. The experimental results definitively demonstrate that the complete APGCN-CF model, which considers both temporal and geographical information simultaneously, consistently outperforms all variants, confirming the efficacy of our proposed spatio-temporal fusion mechanism. Particularly significant is the observation that performance degradation is most severe when both temporal and geographical components are eliminated (APGCN-CF-E), providing compelling evidence for the critical synergistic role of spatio-temporal characteristics in minor agricultural product recommendation. The distinctive performance patterns exhibited by APGCN-CF-C and APGCN-CF-D across different datasets further suggest that the relative importance of temporal versus geographical features may vary according to product-specific characteristics, underscoring the necessity of our comprehensive modeling approach.  
Despite these promising results, several limitations and challenges warrant acknowledgment. The computational complexity of graph convolutional networks may pose scalability constraints as datasets continue to expand. While our hyperparameter analysis provides guidance for optimizing model architecture, the trade-off between model expressiveness and computational efficiency remains a significant challenge, particularly for large-scale rural e-commerce platforms with limited computational resources. Additionally, the current implementation of temporal dynamics modeling may not fully capture the complex seasonal patterns in agricultural product demand, which can be influenced by multiple factors, including holidays, climate variations, and cultural practices. Furthermore, while our framework demonstrates the effective fusion of user and product features, the integration of additional heterogeneous information sources presents both opportunities and challenges. Incorporating contextual information such as detailed product attributes, user demographic data, and environmental factors could potentially enhance recommendation quality but would increase model complexity and computational requirements.