Leveraging Deep Learning for Personalized Fashion Recommendations Using Fashion MNIST

—People are particularly conscious of their clothingchoices since fashion has a big influence on daily life. Largepopulations are usually recommended fashion goods and trendsby specialists via a manual, curated process. On the other hand,e-commerce websites greatly benefit from automatic,personalized recommendation systems, which are becomingmore popular. This study introduces a deep learning-basedframework for personalized fashion recommendation, utilizingthe Fashion-MNIST dataset as the primary data source. Thedataset was divided into training and testing sets in a 70:30 ratioto ensure robust evaluation. CNN, Feedforward NeuralNetworks (FNN), and LSTM models were employed for fashionitem classification. Evaluation metrics such as F1-score, recall,accuracy, precision, and loss, along with confusion matrixanalysis, were utilized to assess model performance. Among thetested models, the CNN demonstrated superior performance,achieving 93.99% accuracy, with F1-score, recall, and precisionall at 94% and a loss value of 0.2037. Comparative analysisfurther highlighted the CNN's effectiveness over FNN andLSTM models. These findings demonstrate the promise of CNNarchitectures for improving the precision and consistency ofindividualized clothing recommendation systems