Published April 6, 2025 | Version v1
Journal article Open

Leveraging Deep Learning for Personalized Fashion Recommendations Using Fashion MNIST

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People are particularly conscious of their clothing choices since fashion has a big
influence on daily life. Large populations are usually recommended fashion goods
and trends by specialists via a manual, curated process. On the other hand, ecommerce websites greatly benefit from automatic, personalized recommendation
systems, which are becoming more popular. This study introduces a deep learningbased framework for personalized fashion recommendation, utilizing the FashionMNIST dataset as the primary data source. The dataset was divided into training
and testing sets in a 70:30 ratio to ensure robust evaluation. CNN, Feed forward
Neural Networks (FNN), and LSTM models were employed for fashion item
classification. Evaluation metrics such as F1-score, recall, accuracy, precision, and
loss, along with confusion matrix analysis, were utilized to assess model performance. Among the tested models, the CNN demonstrated superior
performance, achieving 93.99% accuracy, with F1-score, recall, and precision all at
94% and a loss value of 0.2037. Comparative analysis further highlighted the
CNN's effectiveness over FNN and LSTM models. These findings demonstrate the
promise of CNN architectures for improving the precision and consistency of
individualized clothing recommendation systems. 

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