## 4 Proposed Work
### Framework
* Preference Probability-Based Precise Recommendation Module: This module employs a multi-layer perceptron (MLP) network that has been pre-trained on historical user-agricultural product interaction data to generate refined personalized recommendations. The network processes concatenated user-agricultural product embedding vectors as input and generates probability scores indicating user preferences for minor agricultural products. During the recommendation process, the module performs feature fusion by concatenating user and minor agricultural product embedding vectors, which are subsequently processed through the pre-trained MLP network to model the intricate matching relationships between users and minor agricultural products across multiple neural network layers. The module computes preference probabilities for all candidate minor agricultural products and implements a top-N selection strategy to generate personalized recommendation lists that accurately capture user interests. Through the integration of high-dimensional user and minor agricultural product feature representations, this module effectively encodes user preference patterns, yielding high-quality personalized recommendations. The MLP-based preference modeling architecture demonstrates superior capability in learning complex non-linear user-agricultural product interaction patterns that conventional matching methods struggle to capture, thereby substantially enhancing recommendation accuracy. The proposed framework consists of four interconnected modules that operate sequentially in a pipeline architecture. The graph structure construction module integrates heterogeneous features from multiple sources, including user geographical locations, historical reviews, product nomenclature, and pricing information, to generate unified dimensional embeddings, thereby establishing a standardized data foundation for subsequent processing. The graph convolutional network module implements multi-layer convolutional architectures and message propagation mechanisms to extract deep feature representations of nodes, effectively mitigating the data sparsity challenge. The candidate set generation module employs cosine similarity metrics to identify preference correlations between users, facilitating the rapid construction of personalized candidate pools based on user similarity. The precise recommendation module utilizes a pre-trained multi-layer perceptron network to integrate user-product embedding vectors, generating personalized recommendation lists through sophisticated feature fusion. Specifically, the user similarity-based candidate generation model (USCG) and the preference probability-based precise recommendation model (PPPR) work synergistically, with USCG utilizing efficient cosine similarity methods to rapidly and cost-effectively compress the entire product catalog into a candidate set, performing efficient preliminary screening, enabling PPPR to conduct refined recommendations on a smaller-scale candidate set. This design approach addresses both the computational efficiency and high computational cost issues that may arise in large-scale data application scenarios while fully leveraging the feature vectors output by the graph convolutional network to ensure recommendation accuracy. This modular architecture effectively leverages the inherent advantages of graph neural networks in feature learning and relationship modeling while achieving precise recommendations for minor agricultural products through specialized similarity computations and multi-layer neural networks. The detailed implementation methodologies for each module will be elaborated on in subsequent chapters.