Evaluating Customer Satisfaction in Digital Agricultural Platforms

Padma E., Girija Gayathri M., Gowri Shankar R., Monisha R. M.

2025

Abstract

Traditional collaborative filtering (CF) techniques have been widely successful in e-commerce, especially for suggesting agricultural produce. Many techniques, though, are plagued by inherent disadvantages like data sparsity, cold-start problems, and decreased precision due to the lack of consideration of contextual elements. To overcome these challenges, this paper proposes a Hybrid Deep Learning-based Context-Aware Recommendation System (HDL-CARS) that dynamically balances contextual information through the utilization of user, item, and context embeddings and a sophisticated attention mechanism. By combining deep context-aware analysis, content-based filtering, and collaborative filtering, HDL-CARS identifies subtle, non-linear user-item interactions as well as adjusts to changing parameters like time, location, and user activity. HDL-CARS utilizes state-of-the-art deep neural network models, such as multi-layer perceptrons and attention mechanisms, to improve feature representation and extract hidden patterns from sparse data sets. This process guarantees scalability on different data sizes and flexibility for changing user behavior, making HDL-CARS a perfect candidate for personalized agriculture e-commerce beyond. Empirical tests indicate that traditional CF has a precision and recall of 0.75 and mean absolute error (MAE) of 0.75. By contrast, HDL-CARS drastically enhances accuracy to a precision of 0.85–0.95, recall of 0.90, and smaller MAE of 0.5. These findings demonstrate HDL-CARS's improved accuracy and robustness. With its delivery of highly personalized, real-time recommendations, HDL-CARS improves user experience and relevance, especially in agricultural e-commerce.

Paper Citation

in Harvard Style

E. P., M. G., R. G. and M. M. (2025). Evaluating Customer Satisfaction in Digital Agricultural Platforms. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 298-303. DOI: 10.5220/0013881800004919

in Bibtex Style

@conference{icrdicct`2525,
author={Padma E. and Girija M. and Gowri R. and Monisha M.},
title={Evaluating Customer Satisfaction in Digital Agricultural Platforms},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={298-303},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013881800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Evaluating Customer Satisfaction in Digital Agricultural Platforms
SN - 978-989-758-777-1
AU - E. P.
AU - M. G.
AU - R. G.
AU - M. M.
PY - 2025
SP - 298
EP - 303
DO - 10.5220/0013881800004919
PB - SciTePress