## 2 Related Work
### Minor Agricultural Product Recommendations  
Various recommendation approaches have been proposed and implemented for minor agricultural products. Several studies have focused on developing context-aware recommendation systems, which enhance personalization through the integration of supplementary user data, including geographical location, temporal information, and user activities [15], but their research merely integrated geographical and temporal data without comprehensively modeling users and products (such as incorporating product names, sales volumes, prices, etc., into the framework for thorough analysis). There have been specialized approaches specifically developed for minor agricultural product recommendations incorporating seasonality [16] and regionality [17]; however, these are limited by their information integration approaches, and existing studies often fail to comprehensively model users and products, not fully utilizing available information to enhance recommendation reliability. Some research has also explored recommendation system security, with researchers developing privacy-preserving recommendation systems for minor agricultural products [15]. This study distinguishes itself from previous minor agricultural product recommendation research by proposing a novel approach that incorporates both seasonal and regional sensitivities in minor agricultural product recommendations through the modeling of dynamic item characteristics and leverages the efficient multi-source heterogeneous information integration capability of graph structures and the powerful feature processing capability of graph convolutional networks to achieve comprehensive user-product information modeling and analysis. Unlike general e-commerce products with relatively stable demand patterns, minor agricultural products present distinctive challenges for recommendation systems due to their pronounced spatio-temporal sensitivity. These products are characterized by highly variable seasonal availability, region-specific quality variations, and strong cultural influences on consumption patterns. Traditional recommendation methods often overlap the seasonal characteristics of agricultural products, leading to suboptimal recommendation effects. Research has shown that considering the time effect of agricultural products, seasonal variations, and changes in user interest significantly improves recommendation quality [18]. Food preference is highly personal and varies considerably between individuals, with decisions about what to eat influenced by many biological, personal, and socio-economic factors [19]. In contrast to universally accepted staple agricultural commodities such as wheat, rice, and corn, which exhibit consistent demand across diverse geographical regions, minor agricultural products like scallions and garlic demonstrate significant regional preference variations. For instance, certain types of scallions or garlic varieties may be preferred in specific regions due to long-established culinary traditions and local dietary preferences, creating heterogeneous user preference distributions that are difficult to model using conventional recommendation approaches. This strong regional dependency distinguishes minor agricultural products from major staples, as the latter are incorporated into virtually all regional cuisines with relatively uniform acceptance patterns. Additionally, the purchasing behaviors for these minor products demonstrate significant periodic fluctuations tied to agricultural harvesting cycles,estive seasons, and climate conditions, further complicating the recommendation process. Through a comprehensive review and analysis of existing research, we identify several limitations in traditional recommendation approaches when applied to minor agricultural products, including insufficient feature exploitation and inadequate modeling of dynamic attributes.