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Collaborative Filtering Modification Technology for Recommendation Systems in Smart Digital Agribusiness Marketplace
Corresponding Author(s) : Subiyanto
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 10, No. 3, August 2025
Abstract
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 strengthen 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.
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- M. Vlachopoulou, C. Ziakis, K. Vergidis, and M. Madas, “Analyzing AgriFood-Tech e-Business Models,” Sustainability, vol. 13, no. 10, 2021, doi: 10.3390/su13105516.
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- W. Zhang, Z. Kang, L. Song, and K. Qu, “Graph Attention Interaction Aggregation Network for Click-Through Rate Prediction,” Sensors, vol. 22, no. 24, Dec. 2022, doi: 10.3390/s22249691.
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References
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S. Cook, E. L. Jackson, M. J. Fisher (In Memoriam), D. Baker, and D. Diepeveen, “Embedding digital agriculture into sustainable Australian food systems: pathways and pitfalls to value creation,” Int J Agric Sustain, vol. 20, no. 3, pp. 346–367, May 2022, doi: 10.1080/14735903.2021.1937881.
S. Karande, S. Patil, R. Gharat, A. Mhatre, and P. Sorte, “Application of Intelligent Recommendation for Agricultural Information-E-krishi,” 2023. doi: 10.6084/m9.doione.IJRTI2304116.
E. M. Emeana, L. Trenchard, and K. Dehnen-Schmutz, “The Revolution of Mobile Phone-Enabled Services for Agricultural Development (m-Agri Services) in Africa: The Challenges for Sustainability,” Sustainability, vol. 12, no. 2, 2020, doi: 10.3390/su12020485.
I. F. Gorlov, G. V Fedotova, A. V Glushchenko, M. I. Slozhenkina, and N. I. Mosolova, “Digital Technologies in the Development of the Agro-Industrial Complex,” in Digital Economy: Complexity and Variety vs. Rationality, E. G. Popkova and B. S. Sergi, Eds., Cham: Springer International Publishing, 2020, pp. 220–229. doi: https://doi.org/10.1007/978-3-030-29586-8_26.
D. R. Sari, B. Matsaany, and M. Hamka, “ASPECT EXTRACTION OF E-COMMERCE AND MARKETPLACE APPLICATIONS USING WORD2VEC AND WORDNET PATH,” Jurnal Teknik Informatika (Jutif), vol. 4, no. 4, pp. 787–796, Aug. 2023, doi: 10.52436/1.jutif.2023.4.4.726.
A. N. Fakhriyyah and A. D. Wiranata, “ANALYSIS OF THE LAPAK HIJAU BUSINESS MODEL IN THE SALE OF FURNITURE GOODS THROUGH THE E-COMMERCE PLATFORM,” Jurnal Teknik Informatika (Jutif), vol. 5, no. 4, pp. 711–722, Aug. 2024, doi: 10.52436/1.jutif.2024.5.4.2034.
B. Bunardi, D. Naga, and D. Arisandi, “PENGEMBANGAN APLIKASI E-COMMERCE PRODUK LOKAL DAN DATA KEPENDUDUKAN PADA DESA GIRITENGAH, BOROBUDUR,” Computatio : Journal of Computer Science and Information Systems, vol. 3, p. 77, Jun. 2019, doi: 10.24912/computatio.v3i1.4274.
O. Al-Shamaileh and A. Sutcliffe, “Why people choose Apps: An evaluation of the ecology and user experience of mobile applications,” Int J Hum Comput Stud, vol. 170, p. 102965, 2023, doi: https://doi.org/10.1016/j.ijhcs.2022.102965.
S. P. Thar, T. Ramilan, R. J. Farquharson, A. Pang, and D. Chen, “An empirical analysis of the use of agricultural mobile applications among smallholder farmers in Myanmar,” THE ELECTRONIC JOURNAL OF INFORMATION SYSTEMS IN DEVELOPING COUNTRIES, vol. 87, no. 2, p. e12159, Mar. 2021, doi: https://doi.org/10.1002/isd2.12159.
M. Ariandi and M. N. Risqi, “ANALYSIS OF ANDROID BASED PALEMBANG BIBIK SAYUR APPLICATION USING USABILITY TESTING,” Jurnal Teknik Informatika (Jutif), vol. 3, no. 6, pp. 1791–1802, Dec. 2022, doi: 10.20884/1.jutif.2022.3.6.672.
S. Wijaya, A. Andhika, and M. Ilyas, “DEVELOPMENT OF SALES INFORMATION SYSTEM FOR SME WITH THE WATERFALL METHOD: A GROCERY STORE BSR CASE,” Jurnal Teknik Informatika (Jutif), vol. 3, no. 4, pp. 1043–1049, Aug. 2022, doi: 10.20884/1.jutif.2022.3.4.263.
N. Rahanra, D. Erlianti, Rissa Megavitry, D. J. A. Butarbutar, and Z. HB, “DESIGN AND DEVELOPMENT OF ANDROID-BASED TRADITIONAL INDONESIAN CLOTHING IMAGE GUESSING GAME,” Jurnal Teknik Informatika (Jutif), vol. 4, no. 1, pp. 199–203, Feb. 2023, doi: 10.52436/1.jutif.2023.4.1.377.
L. Wu, X. He, X. Wang, K. Zhang, and M. Wang, “A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation,” IEEE Trans Knowl Data Eng, vol. 35, no. 5, pp. 4425–4445, 2023, doi: 10.1109/TKDE.2022.3145690.
S.-H. Liao, R. Widowati, and Y.-C. Hsieh, “Investigating online social media users’ behaviors for social commerce recommendations,” Technol Soc, vol. 66, p. 101655, 2021, doi: https://doi.org/10.1016/j.techsoc.2021.101655.
V. H. Pham, A. T. Nguyen, B. T. Phung, and T. H. V. Phan, “Developing a restaurant recommended system via the Vietnamese food image classification,” International Journal of Electrical and Computer Engineering, vol. 14, no. 2, pp. 1711–1719, 2024, doi: 10.11591/ijece.v14i2.pp1711-1719.
Z. Shokrzadeh, M.-R. Feizi-Derakhshi, M.-A. Balafar, and J. Bagherzadeh Mohasefi, “Knowledge graph-based recommendation system enhanced by neural collaborative filtering and knowledge graph embedding,” Ain Shams Engineering Journal, vol. 15, no. 1, p. 102263, 2024, doi: https://doi.org/10.1016/j.asej.2023.102263.
H. Liu et al., “EDMF: Efficient Deep Matrix Factorization With Review Feature Learning for Industrial Recommender System,” IEEE Trans Industr Inform, vol. 18, no. 7, pp. 4361–4371, 2022, doi: 10.1109/TII.2021.3128240.
S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “BPR: Bayesian Personalized Ranking from Implicit Feedback,” arXiv (Cornell University), 2012, doi: 10.48550/arxiv.1205.2618.
Z. Lyu, Y. Wu, J. Lai, M. Yang, C. Li, and W. Zhou, “Knowledge Enhanced Graph Neural Networks for Explainable Recommendation,” IEEE Trans Knowl Data Eng, vol. 35, no. 5, pp. 4954–4968, 2023, doi: 10.1109/TKDE.2022.3142260.
Y. Zhang et al., “BI-GCN: Bilateral Interactive Graph Convolutional Network for Recommendation,” in International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, Oct. 2023, pp. 4410–4414. doi: 10.1145/3583780.3615232.
H. Tang, G. Zhao, X. Bu, and X. Qian, “Dynamic evolution of multi-graph based collaborative filtering for recommendation systems,” Knowl Based Syst, vol. 228, p. 107251, 2021, doi: https://doi.org/10.1016/j.knosys.2021.107251.
S. Li et al., “Region-aware neural graph collaborative filtering for personalized recommendation,” Int J Digit Earth, vol. 15, no. 1, pp. 1446–1462, Dec. 2022, doi: 10.1080/17538947.2022.2113463.
K. Shi, J. Zhang, L. Fang, W. Wang, and B. Jing, “Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems,” arXiv (Cornell University), 2024.
W. Zhang, Z. Kang, L. Song, and K. Qu, “Graph Attention Interaction Aggregation Network for Click-Through Rate Prediction,” Sensors, vol. 22, no. 24, Dec. 2022, doi: 10.3390/s22249691.
X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua, “Neural Graph Collaborative Filtering,” in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA: ACM, Jul. 2019, pp. 165–174. doi: 10.1145/3331184.3331267.
D. Peng, S. J. Pan, J. Zhang, and A. Zeng, “Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems,” arXiv (Cornell University), 2021, doi: 10.1145/3460231.3474239.
S. Z. U. Hassan, M. Rafi, and J. Frnda, “GCZRec: Generative Collaborative Zero-Shot Framework for Cold Start News Recommendation,” IEEE Access, vol. 12, pp. 16610–16620, 2024, doi: 10.1109/ACCESS.2024.3359053.