Collaborative Filtering Modification Technology for Recommendation Systems in Smart Digital Agribusiness Marketplace

Arif Prabowo, Setya Budi
Subiyanto
Azis Salim, Nur

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Vol. 10, No. 3, August 2025

The rapid transformation in the agribusiness sector, driven by globalization and digitalization, necessitates the adoption of intelligent systems to enhance performance, market accessibility, and decision-making processes. Despite the growing use of personalized recommender systems in e-commerce, geographical context remains insufficiently integrated into recommendation processes. This lack of geolocation awareness diminishes recommendation relevance and accuracy by overlooking geographical factors that influence user preferences. To address this limitation, this work aims to enhance the performance of recommendation systems in agricultural e-commerce by incorporating geolocation context through the integration of the Geo-Mod Neuro Collaborative Filtering (GMNCF) model into an Android-based application for agricultural products. The GMNCF model improves collaborative filtering by incorporating geographical region data to capture spatial user preferences and reduce data sparsity. Using Graph Neural Networks (GNNs), the model captures complex relationships among users, items, and geographic regions to generate more accurate recommendations. Experimental results reveal that GMNCF consistently delivers substantial performance improvements over baseline models such as NGCF, GC-MC, ASMG, and GCZRec. Compared to the strongest baselines, GMNCF demonstrates relative gains of approximately 4.9% in Precision, 5.9% in Recall, 5.6% in F1-Score, and 5.7% in Hit Rate. These improvements underscore the model’s effectiveness in capturing spatially influenced user preferences and strengthening the relevance of recommendations in the agribusiness e-commerce system. Furthermore, user testing with diverse respondents indicates high levels of satisfaction, particularly regarding location-based recommendation features and accessibility. These findings highlight the effectiveness of incorporating geographical region data into recommendation systems, which is particularly beneficial for geographically fragmented agribusiness markets.