## 4 Proposed Work  
### Framework  
With the advancement of mobile internet technologies, rural e-commerce platforms have emerged as crucial channels for minor agricultural product distribution. However, conventional recommendation approaches face significant challenges in delivering precise, personalized recommendations due to the distinct characteristics of minor agricultural product sales, including strong seasonal and regional dependencies coupled with heterogeneous user preferences. As demonstrated by [PERSON] et al. [15], graph neural networks (GNNs) demonstrate superior capability in capturing complex higher-order interaction patterns between users and items through iterative feature propagation mechanisms that aggregate embedding representations from neighboring nodes. Leveraging this advantage, this study presents a minor agricultural product personalized recommendation framework that integrates graph convolutional networks with collaborative filtering (APGCN-CF). The proposed framework achieves precise adaptation to minor agricultural product recommendation scenarios through the construction of unified dimensional homogeneous graph structures and the implementation of specialized similarity computation modules. As shown in Figure 1, APGCN-CF consists of four closely integrated functional modules. * Graph Structure Construction Module: This module generates unified dimensional embedding representations by integrating diverse node and edge features. For user nodes, it incorporates geographic location encodings and historical review text embeddings. For item nodes, it processes item name embeddings and sales price information. Edge features encompass user-item interactions, including ratings and review timestamps. Through systematic feature dimension alignment, this module transforms heterogeneous information into a homogeneous graph structure amenable to graph convolution operations. This unified data representation framework establishes a robust foundation for subsequent deep feature learning processes. Figure 1: Framework of APGCN-CF. * Graph Convolutional Network Module: This module employs the constructed homogeneous graph as input to perform node representation learning through carefully designed multi-layer graph convolutional networks. During the convolution process, each layer iteratively updates and optimizes the hidden representations of nodes through sophisticated message passing and feature aggregation mechanisms. Following multiple layers of propagation, the module generates user and item node embedding vectors that effectively capture high-order interaction information within the graph structure. As demonstrated by [PERSON] et al. [15], feature learning based on graph structures can significantly mitigate the data sparsity challenge, primarily because graph neural networks can comprehensively leverage high-order neighborhood information through their inherent message-passing mechanisms. * Candidate Set Generation Module Based on User Similarity: This module utilizes user embedding vectors derived from graph convolutional networks to construct high-quality candidate sets through inter-user similarity analysis. The generation process employs cosine similarity metrics to identify the most similar user clusters for each target user, subsequently aggregating historical interaction items from these similar users to form comprehensive candidate sets. By systematically mining interaction patterns from users with similar preferences, the module generates personalized candidate pools that effectively capture potential user interests. The implementation of computationally efficient cosine similarity calculations results in low computational overhead, enabling rapid preliminary screening of candidate items and establishing a robust foundation for subsequent refined personalization in recommendation modules.