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

Author

Listed:
  • 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))

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.

Suggested Citation

  • Nawreen Anan Khandaker & Amrin Rahman & Amrin Akter Pinky & Tasmiah Tamzid Anannya, 2025. "A Deep Convolutional Neural Network-Based Approach for Visual Search & Recommendation of Grocery Products," Annals of Data Science, Springer, vol. 12(3), pages 877-897, June.
    • Handle: RePEc:spr:aodasc:v:12:y:2025:i:3:d:10.1007_s40745-024-00540-5
    DOI: 10.1007/s40745-024-00540-5
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