Deep Learning-based Recommendation System for Personalized Product Recommendations

Malik, Sagar (2023) Deep Learning-based Recommendation System for Personalized Product Recommendations. Masters thesis, Dublin, National College of Ireland.

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Abstract

Personalized product recommendations have become important in today’s highly driven online market, as they significantly contribute to improving user experience and maintaining customer fulfillment. This research presents an innovative methodology to developing a customized recommendation system specifically designed for the fashion industry. This methodology integrates deep learning methodologies with traditional machine learning methods. The primary objective of this research was to investigate the effective application of pre-trained deep learning models, specifically ResNet50, for extracting features from fashion datasets. The objective is to generate comprehensive and relevant product descriptions. The approach aims to use pre-trained models such as ResNet50 to extract significant patterns from images, and takes consideration variables such as consumer gender, past product history, and other various attributes in the procedure for customization. This ensures that the recommended products match to individual preferences. In order to improve the quality of recommendations, one can apply approaches such as K-Nearest Neighbors (KNN) using different distance measures and a variety of machine learning models to discover similar products. The preliminary evaluation demonstrates the accessibility and accuracy of the proposed system, demonstrating its ability to generate personalized fashion recommendations for specific users. This research can significantly enhance personalized recommendation systems, benefiting both e-commerce platforms and consumers.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Basillio, Jorge UNSPECIFIED
Uncontrolled Keywords:	Deep Learning; personalized recommendation; Machine learning; ResNet50; KNN
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	16 May 2025 10:38
Last Modified:	16 May 2025 10:38
URI:	https://norma.ncirl.ie/id/eprint/7564

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