## 5 Experimental Evaluation
### Performance Comparison
#### 5.5.3 Experiment 3  
This section presents a comprehensive ablation study examining the influence of spatio-temporal features within the APGCN-CF framework. Given that minor agricultural products exhibit distinctive temporal and spatial characteristics, understanding the contribution of these features to recommendation performance is essential for developing effective recommendation systems in rural e-commerce platforms.  
To systematically evaluate the impact of spatio-temporal components, we constructed three model variants: APGCN-CF-C (incorporating geographical information while excluding temporal features), APGCN-CF-D (retaining temporal patterns while eliminating geographical information), and APGCN-CF-E (removing both temporal and geographical components). Figure 5 illustrates the comparative performance of these variants against the complete APGCN-CF model across both Garlic and Scallion datasets.  
The experimental analysis reveals the consistent superiority of the complete APGCN-CF model over all three variants across multiple evaluation metrics, thereby demonstrating the critical importance of integrating both temporal and geographical information for precise minor agricultural product recommendations. In the garlic dataset evaluation, APGCN-CF-C exhibited notably better performance than APGCN-CF-D, suggesting that geographical features may play a more significant role in modeling user preferences for this particular product. This pattern was similarly observed in the scallion dataset, albeit with  
Figure 4: Comparative analysis of the recommendation performance between APGCN-CF and APGCN-CF-B.  
reduced magnitude, indicating that the dependency on specific spatio-temporal features may vary across different categories of minor agricultural products.  
Particularly noteworthy is the substantial performance degradation observed in APGCN-CF-E, where the simultaneous removal of both temporal and geographical components resulted in significantly reduced F1-scores for top-10 recommendations across both datasets. This finding provides compelling evidence for the synergistic effect of spatio-temporal features in enhancing recommendation quality for minor agricultural products.  
The ablation study empirically validates our research hypothesis regarding the essential role of spatio-temporal characteristics in minor agricultural product recommendation. Temporal features facilitate the capture of seasonal variations in product availability and demand patterns, while geographical components enable the model to address regional preferences influenced by culinary traditions and agricultural production cycles. The superior performance demonstrated by the complete APGCN-CF model confirms that our framework effectively leverages these spatio-temporal dependencies to generate more accurate and contextually relevant recommendations for minor agricultural products in rural e-commerce environments, thereby addressing the unique challenges posed by the pronounced seasonality and regional specificity inherent to these products.  
Figure 5: Comparative analysis of the recommendation performance between APGCN-CF and its spatio-temporal variants (APGCN-CF-C, APGCN-CF-D, and APGCN-CF-E).