## 1 Introduction
Consequently, the accurate integration of temporal and geographical information into the recommendation process, coupled with the ability to dynamically adjust recommendation results accordingly, presents a significant challenge in minor agricultural product recommendation systems. * Data Sparsity and Cold-Start Problems: Data sparsity and cold-start problems present significant challenges in minor agricultural product recommendation systems. The extremely limited user-item interaction data lead to severe sparsity issues in the data matrix. Furthermore, the cold-start problem, which occurs when new users enter the system or new products are introduced, compounds these challenges by lacking historical interaction data for accurate recommendations. To address these challenges, we propose a minor agricultural product personalized recommendation framework integrating graph convolutional networks and collaborative filtering (APGCN-CF). This novel framework is specifically designed to generate personalized product recommendation lists that accurately reflect users' preferences in minor agricultural product purchasing. By leveraging graph convolutional neural networks, our framework effectively aggregates and fuses latent features from users and their neighboring nodes in the user-product interaction graph. Through comprehensive analysis of historical user-product interaction data, the framework precisely captures and models user preferences for minor agricultural products, ultimately generating highly personalized recommendation lists that align with individual user preferences. The innovation of our approach lies not in any single component in isolation but in the synergistic integration of four carefully designed modules: graph structure construction, convolution-based feature learning, user similarity-based candidate generation, and preference probability-driven precise recommendation, creating a comprehensive pipeline specifically tailored to address the unique challenges in minor agricultural product recommendation. Furthermore, we construct a comprehensive graph structure that incorporates the essential characteristics of minor agricultural products and user profiles and review behavioral patterns. This structure enables in-depth analysis of both seasonal and regional attributes of minor agricultural products, thereby effectively mitigating the impact of seasonality and geographical constraints on purchasing patterns. The proposed graph-based approach efficiently captures the temporal and spatial correlations between users and products, significantly enhancing the expressiveness of feature representations. By leveraging the rich interconnections between user preferences and product attributes within the graph structure, our method effectively addresses the data sparsity challenge inherent in agricultural recommendation systems. Moreover, through the integration of historical user reviews and multi-dimensional product feature information, the system successfully mitigates the cold-start problem for both newly registered users and newly listed minor agricultural products. To evaluate the effectiveness of APGCN-CF, we conduct comprehensive experiments using real-world minor agricultural product datasets, specifically focusing on scallions and garlic as representative cases. These seasonings were strategically selected due to their distinctive characteristics in agricultural e-commerce: they exhibit significant seasonal harvesting patterns that directly impact availability and pricing, their consumption demonstrates strong regional preferences influenced by local culinary traditions, and unlike staple agricultural commodities with universal demand patterns. These minor products present unique recommendation challenges due to their heterogeneous user preference distributions.