Fashion Recommendation System Using Machine Learning

For the past few years, fast fashion has become very popular, which has had a great impact on the textile and fashion industries. Fashion is an integral part of one's daily lives, and it has a significant impact on identity and self-expression. With the increasing availability of digital platforms and e-commerce websites, the fashion industry has transformed, and the way one shops for clothing has evolved. The rise of e-commerce has brought about a rise in the use of fashion recommendation systems. These systems aim to offer personalized product recommendations to users according to their interests and preferences. The field of machine learning has made significant strides in the development of image processing techniques, parsing techniques, image classification, segmentation, and networking, making it an ideal candidate for powering these recommendation systems. With these advancements in technology, the potential for fashion recommendation systems to provide even more accurate and individualized product recommendations is substantial. In this study, the dataset is used and the main technique is to use machine learning by splitting into training and testing data of the dataset. The convolutional neural network is used to produce similar items in the recommendation system. The CNN layers are upgraded by using RESNET50 and the filtering content is based on data provided for the product. The RESNET50 helps in overcome the problem of vanishing gradient. Then KNN algorithm is used to recommend similar items. The main idea behind KNN is Euclidean distance and Cosine Similarity which helps in producing similar products. The user's past behavior is significant here and the convolutional neural networks (CNN) model was also utilized for picture categorization and recognition. The experimental results of the system achieved retrieval accuracy and outperformed the baseline.