Clothing Recommender System using Deep Learning
Keywords: Convolution Neural Network, Amazon Web Service, Artificial Intelligence, Collabarative Deep Learning.
Clothing recommendation systems are gaining publicity in the ecommerce industry as a way to provide users with personalized recommendations and improve their shopping experience. These systems use a variety of techniques, including collaborative filtering, content-based masking, and hybrid approaches, to recommend clothes that are compatible with the user's preferences, styles, and some other relevant factors. We propose in this paper a clothes recommender system that combines content - based filtering and information filtering to provide users with accurate and relevant recommendations. This same collaborative filtering component identifies similar users or items based on user-item interaction data, whereas the content-based filtration component finds similar items based on item attributes such as brand, colour, size, and style.
Deep learning has achieved notable success in a variety of application areas, including recommendation systems. We propose a cloth recommender system that uses deep learning methods to provide personalised suggestions for users based on their preferences, styles, and some other relevant factors in this paper. To acquire the visual features of clothing items such as colour, pattern, and style from images, our system employs a deep neural network architecture, specifically a non - linear and non-convolutional neural network (CNN). The CNN is trained on a large data source of clothing images, and so it learns to extract meaningful features from the images automatically, which are then used to make recommendations.
Finally, our deep learning-based cloth recommender system combines visual features are extracted from fashion images of background knowledge to provide personalised and accurate recommendations. The system outperforms traditional recommendation methods by taking advantage of deep neural networks' powerful feature representation capabilities. It has the potential to significantly improve users' shopping experiences and build strong customer relationships on e-commerce platforms.