International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 3 (May-June 2025) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.

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

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