Fashion Recommendation System Using Deep Learning

Recommendation systems are an important aspect of e-commerce as they improve user experience and boost sales through personalized product recommendations. This project aims to create an image-based recommendation system based on transfer learning, where the ResNet model is utilized for feature vector extraction and the ANNOY algorithm for fast image retrieval. Through the analysis of product images, the system retrieves and suggests the five products most similar to a query image. Unlike traditional methods of statistics and similarity measurements, transfer learning facilitates the learning process of the complex patterns of the product image data set, and ANNOY provides the capabilities of efficient retrieval of similar content at fast scales, giving higher accuracy and visual-relevance of recommendations. Through this mechanism, the searching process in the extensive data set can be substantially made faster. As e commerce becomes increasingly widespread, this picture-driven recommendation algorithm provides a richer and more tailored buying experience to customers.