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

Q1 Decision Sciences
Nawreen Anan Khandaker, Amrin Rahman, Amrin Akter Pinky, Tasmiah Tamzid Anannya
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引用次数: 0

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.

Abstract Image

基于深度卷积神经网络的杂货产品视觉搜索与推荐方法
搜索和推荐是任何电子商务网站查找和购买特定产品的两个基本功能。与基于文本的搜索方法相比,视觉搜索是一种有前途的快速搜索方法。因此,本研究的目的是提出一个概念框架,用于使用CNN模型的集成学习开发杂货产品的视觉搜索和推荐系统。传统的深度学习和集成学习技术分别在包含3174张和3162张图像的公开数据集和自制数据集上实现。使用从研究结果中找到的合适模型的各种组合来找到搜索和推荐功能的最佳拟合模型。所有模型都使用合适的性能指标进行评估,集成学习方法表现更好。结合VGG16和MobileNet获得了最佳的视觉搜索结果,分类准确率为99.8%,在产品推荐方面,MobileNet和ResNET50的组合表现优于其他技术。
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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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