Personalized fashion product recommendations using transfer learning and nearest neighbors models

Abstract

In the fashion retail e-commerce sector, personalized product recommendations are crucial for enhancing the shopping experience. This study introduces a method that combines a pre-trained deep learning model named VGG19 with the 10 nearest neighbors algorithm to recommend visually similar products. VGG19 is utilized to extract detailed features from product images, enabling more accurate recommendations. The nearest neighbors algorithm then selects the ten products most similar to those previously viewed by customers. Recommendations are ranked based on customer purchase frequency to prioritize the most popular and relevant items. This method's practical applicability was demonstrated by testing it on a diverse set of products, including jackets from outerwear, baby bodysuits from children's wear, socks from footwear, and sunglasses from the accessories category.