Leveraging Deep Learning for Personalized Fashion Recommendations Using Fashion MNIST

People are particularly conscious of their clothing choices since fashion has a biginfluence on daily life. Large populations are usually recommended fashion goodsand trends by specialists via a manual, curated process. On the other hand, ecommerce websites greatly benefit from automatic, personalized recommendationsystems, 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 trainingand testing sets in a 70:30 ratio to ensure robust evaluation. CNN, Feed forwardNeural Networks (FNN), and LSTM models were employed for fashion itemclassification. Evaluation metrics such as F1-score, recall, accuracy, precision, andloss, along with confusion matrix analysis, were utilized to assess model performance. Among the tested models, the CNN demonstrated superiorperformance, achieving 93.99% accuracy, with F1-score, recall, and precision all at94% and a loss value of 0.2037. Comparative analysis further highlighted theCNN's effectiveness over FNN and LSTM models. These findings demonstrate thepromise of CNN architectures for improving the precision and consistency ofindividualized clothing recommendation systems.