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Annals of Data Science

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

  • 23-05-2024
Published in:
Authors
Nawreen Anan Khandaker
Amrin Rahman
Amrin Akter Pinky
Tasmiah Tamzid Anannya
Published in
Annals of Data Science | Issue 3/2025

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Abstract

The proliferation of e-commerce websites has led to an increasing demand for efficient search engines. While text-based search methods are common, they often fail to meet user needs, especially when dealing with synonyms or different languages. This article introduces a deep learning-based approach for visual search and recommendation of grocery products, leveraging convolutional neural networks (CNNs) and ensemble learning to overcome the limitations of text-based searches. The proposed method involves feature extraction using pre-trained models and stacking ensemble models for product classification and recommendation. The authors evaluate the performance of their approach using various datasets and metrics, demonstrating promising results in terms of accuracy, precision, recall, and F1 score. The study also highlights the potential of visual search and recommendation systems in enhancing user experience on e-commerce platforms. By combining traditional machine learning techniques with advanced deep learning models, this research offers a comprehensive solution to improve the efficiency and convenience of online grocery shopping.
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Title
A Deep Convolutional Neural Network-Based Approach for Visual Search & Recommendation of Grocery Products
Authors
Nawreen Anan Khandaker
Amrin Rahman
Amrin Akter Pinky
Tasmiah Tamzid Anannya
Publication date
23-05-2024
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 3/2025
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-024-00540-5
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