A Deep Convolutional Neural Network-Based Approach for Visual Search & Recommendation of Grocery Products

Nawreen Anan Khandaker (), Amrin Rahman (), Amrin Akter Pinky () and Tasmiah Tamzid Anannya ()
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Nawreen Anan Khandaker: Military Institute of Science and Technology (MIST)
Amrin Rahman: Military Institute of Science and Technology (MIST)
Amrin Akter Pinky: Military Institute of Science and Technology (MIST)
Tasmiah Tamzid Anannya: Military Institute of Science and Technology (MIST)

Annals of Data Science, 2025, vol. 12, issue 3, No 3, 877-897

Abstract: Abstract Search and recommendation are two essential features of any e-commerce website for finding and purchasing a specific product. Visual Search is a promising and quick method in comparison to a textual-based search method. Hence, the objective of this research is to propose a conceptual framework for developing a visual search and recommendation system for grocery products using Ensemble Learning with CNN models. Traditional Deep learning and Ensemble Learning techniques were implemented with a publicly available and a self-made data set containing 3174 and 3162 images respectively. Various combinations of the suitable models found from research findings were used to find the best-fitted model for both the search and recommendation functionalities. All the models were evaluated using suitable performance metrics and the Ensemble Learning approach performed better. The best-performed results for visual searching are obtained by incorporating VGG16 and MobileNet with an accuracy of 99.8% for classification and in the case of product recommendation, the combination of MobileNET and ResNET50 performs better than other techniques.

Keywords: Visual search; Recommendation; Ensemble learning; Deep learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s40745-024-00540-5

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