Work

APGCN-CF: A Spatio-Temporal-Aware Graph Convolutional Network Framework for Minor Agricultural Product Recommendation in Rural E-Commerce

Year: 2025

Type: article

Abstract: This study introduces APGCN-CF, an innovative graph convolutional network-based framework designed to address the distinctive challenges of minor agricultural product recommendation in rural e-commerce environments. The framework synergistically integrates graph convolutional networks (GCNs) with collaborative filtering (CF), providing an effective solution to critical challenges in minor agricultural product recommendations. APGCN-CF implements a multi-module architecture encompassing graph structure construction, convolution-based feature learning, user similarity-based candidate generation, and preference probability-driven precise recommendation. Comprehensive experimental evaluations conducted on real-world minor agricultural product datasets, featuring scallions and garlic as representative cases, demonstrate that APGCN-CF consistently outperforms existing state-of-the-art baseline methods across multiple performance metrics, including precision, recall, and F1-score. Notably, the framework exhibits superior performance in challenging scenarios characterized by data sparsity and cold-start conditions through its effective feature extraction and relationship modeling capabilities. Through deep integration of user preference features, APGCN-CF enables the dynamic generation of highly personalized recommendation lists, providing robust decision support for optimizing minor agricultural product sales. This research not only advances the theoretical landscape of recommendation systems but also delivers a practical solution tailored for personalized recommendation within rural e-commerce platforms.

Source: Applied Sciences

DOI: 10.3390/app15084362

Authors: Wenjin Hou, Ke Zhu, Pingzeng Liu, Honghua Jiang, Yan Zhang, Xinran Yu

Institution: Shandong Agricultural University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $2,896