APPAREL RECOMMENDATION SYSTEM FOR E-COMMERCE USING MACHINE LEARNING, DEEP LEARNING, CNN, TENSORFLOW, RESNET , AND VTO

This article presents an advanced Apparel Recommendation System that seeks to improve the experience of online shopping using

advanced technologies such as Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks (CNNs),

TensorFlow, and ResNet. The system scrutinizes user preferences and product information in depth to produce precise and

relevant product recommendations. CNNs become significant in extracting primary visual features such as style and color from

garment images, while ResNet models contribute to enhancing the accuracy of feature extraction, especially in dealing with

intricate patterns. The recommendation engine uses both collaborative and content-based filtering techniques with the support of

machine learning algorithms to make advanced and adaptive recommendations. Experimental findings show significant

improvements in the precision of recommendations, thus delineating the potential of the system in simplifying the shopping

process and improving overall user satisfaction.