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

Author(s)	Mr. Kartikey Pandey, Ms. Shruti Sharma, Mr. Rahul Singh, Ms. Shreya Sharma, Ms. Harshika Sharma, Prof. Uttam Singh
Country	India
Abstract	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.
Keywords	Apparel Recommendation, Content-Based Filtering, Personalized Recommendations, Machine Learning, Deep Learning, CNN, TensorFlow, ResNet.
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-09
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.44308
Short DOI	https://doi.org/g9kfkm

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International Journal For Multidisciplinary Research