## 1 Introduction
As evidenced by [PERSON] and [PERSON] [4], consumer purchasing behavior is shaped by both internal and external factors, where internal factors constitute the foundation of purchase intention, while external factors act as catalytic conditions. In the context of rural e-commerce platforms, this study specifically focuses on scallions and garlic as representative minor agricultural products to conduct a comprehensive analysis of their sales patterns and characteristics. These ingredients, being fundamental seasonings in Chinese cuisine, demonstrate not only extensive culinary applications but also maintain consistent and widespread market demand, thereby serving as ideal candidates for investigating the sales models of minor agricultural products in rural e-commerce. Through the development of a user-agricultural product preference model, this research aims to systematically analyze users' consumption preferences and subsequently generate accurate, personalized product recommendations. This approach ultimately facilitates precise recommendation generation and sales optimization within the rural e-commerce ecosystem. Recommender systems have emerged as one of the most effective tools for mining user preferences and implementing precise recommendations for online resources. These systems have been extensively deployed in information management systems across diverse domains, including movies, music, videos, and commercial products. In these applications, recommender systems have demonstrated remarkable capabilities in accurately extracting user preferences, efficiently managing vast resource repositories for recommendations, and optimizing marketing strategies. Therefore, the integration of recommender systems into rural e-commerce platforms for mining user preferences in minor agricultural products and implementing precise recommendations provides robust theoretical support for developing targeted sales and recommendation mechanisms in rural e-commerce ecosystems. Nevertheless, the implementation of recommender systems in the agricultural domain, particularly for minor agricultural products, encounters several significant challenges for the following reasons:  
* Capturing Diverse User Preferences with Precision: Users exhibit significant variations in their preferences for minor agricultural products, influenced by factors such as dietary habits and cooking practices. Previous research has demonstrated that analyzing historical interaction patterns is an effective approach to modeling user preferences. For instance, [PERSON] et al. [5] introduced FISMs (factored item similarity models), which leverage implicit item similarity relationships to capture user preferences and have demonstrated superior performance in top-N recommendation tasks. However, in the context of minor agricultural product recommendations, developing comprehensive and granular methods to capture and analyze such diverse preferences for more accurate preference modeling remains a significant research challenge. * Integration of Heterogeneous Information Poses Significant Challenges: The incorporation of contextual user information has emerged as a crucial strategy in delivering personalized minor agricultural product recommendations. However, the recommendation process inherently involves processing extensive heterogeneous data sources, encompassing both user-specific attributes and minor agricultural product characteristics. This heterogeneity substantially increases the complexity of the recommendation system. Consequently, the effective integration of such diverse information streams into a unified computational model remains a significant technical challenge in the field. * Temporal-Spatial Sensitivity and Dynamic Adjustment: The demand for minor agricultural products demonstrates distinctive temporal and spatial characteristics, manifesting in significant seasonal and regional variations. These characteristics can result in substantial fluctuations or temporary cessation of user demand for specific products during particular time periods.