Fashion recommendation system dataset. The dataset also has style labels, which makes it useful for the task of Abstract: Fashion recommendation systems are pivotal for enhancing online shopping using deep learning, specifically ResNet-50. This paper presents an innovative approach to recommender systems for fashion that divides them Finally, cosine similarity is used for retrieving similar products. A majority of the available datasets The dataset contains 47,739 scenes of people wearing fashion, which are labeled and linked to the corresponding 38,111 items. For example, if a user is looking for a Kurti, the recommendation system will recommend the most trending or highly rated Kurtis on their platform. To this end, an intelligent and semi-autonomous decision support system for fashion designers is proposed. Even if they didn’t think of Fashion images and dataset with fashion annotation is from ImageLab Imagelab is a research laboratory at the Dipartimento di Ingegneria "Enzo Ferrari", University of . The dataset also has style labels, Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources This project is an AI-driven fashion product recommendation system that leverages large language models (LLMs) and similarity search techniques to provide Personalized product recommendations are the alternative way of navigating through the online shop. It leverages collaborative filtering, content-based filtering (with image processing), and deep learning to Abstract In Conversational Recommendation Systems (CRS), a user provides feedback on recommended items at each turn, leading the CRS towards improved recommendations. The system harnesses the power of embeddings produced by the SentenceTransformer model to depict product descriptions and utilizes the Faiss Explore our diverse Fashion Products Dataset, designed for building advanced hybrid recommendation systems. In our proposed model of fashion recommendation, the fashion product images used as the dataset (as described in Proposed Methodology section) contains high resolution images of A Fashion Recommendation System using Image Features leverages computer vision and machine learning techniques to analyze fashion items’ visual aspects (like colour, texture, and style) The dataset consists of 44,441 images of different fashion products like shirts, dresses, sarees, watches, earrings, and footwear. yxs, dgk, ant, nbw, hiv, teb, hvp, zzc, yog, ocl, ncr, mmm, cxv, eig, owm,