Project information
- Category: Recommender System
- Client: N/A
- Project date: 8 April, 2023
- Project URL: Fashion Recommender
- Technology Stack Used: Python
Fashion Products Recommender System
The Content-Based Product Recommender System with Reverse Image Search is a web application developed using Streamlit and Python that provides personalized product recommendations based on uploaded images. The system leverages deep learning techniques and pre-trained ResNet50 model to extract features from the uploaded image, representing its visual characteristics. The extracted features are then compared to a database of product features using the cosine similarity metric to find the most similar products.
The application allows users to upload an image of a product they like and want recommendations for. The uploaded image is displayed on the interface, and the system extracts its visual features using the ResNet50 model. The extracted features are then compared to the existing product features in the database using the cosine similarity metric. The system identifies the most similar products based on the calculated similarities.
The top 6 similar products are displayed on the user interface, along with their corresponding images. Each recommended product is labeled and shown with its respective image. This allows users to explore and discover similar products based on their preferences and visual similarity.
The Content-Based Product Recommender System with Reverse Image Search is a powerful tool for e-commerce platforms and users who are looking for visually similar products. It helps users find products that match their preferences based on the visual characteristics of the uploaded image, providing an enhanced and personalized shopping experience.