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Annals of Data Science

23.05.2024

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

verfasst von: Nawreen Anan Khandaker, Amrin Rahman, Amrin Akter Pinky, Tasmiah Tamzid Anannya

Erschienen in: Annals of Data Science

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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.

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Titel: A Deep Convolutional Neural Network-Based Approach for Visual Search & Recommendation of Grocery Products
verfasst von: Nawreen Anan Khandaker
Amrin Rahman
Amrin Akter Pinky
Tasmiah Tamzid Anannya
Publikationsdatum: 23.05.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Annals of Data Science
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-024-00540-5

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