Ecommerce-product-recommendation-system and E-commerce_recommendation_system
These are competitors: both implement collaborative filtering and content-based recommendation engines for e-commerce platforms, with Project A offering a pure ML recommendation system and Project B wrapping it in a Django web application, making them alternative approaches to solving the same product recommendation problem rather than tools designed to work together.
About Ecommerce-product-recommendation-system
Vaibhav67979/Ecommerce-product-recommendation-system
Product Recommendation System is a machine learning-based project that provides personalized product recommendations to users based on their interaction history, similar users, and also the popularity of products.
Implements three distinct recommendation approaches: rank-based filtering for cold-start problems, similarity-based collaborative filtering using cosine similarity, and model-based SVD decomposition on sparse matrices to predict user ratings across 50 latent features. Trained on Amazon electronics ratings data, the system converts user-product interactions into compressed sparse row matrices to optimize memory usage, then evaluates predictions using RMSE to measure accuracy of unrated product recommendations.
About E-commerce_recommendation_system
ShawonBarman/E-commerce_recommendation_system
This Django-based E-commerce recommendation system uses machine learning models to provide product recommendations based on user input and similarity scores. It scrapes data from Amazon, preprocesses it, and displays product recommendations in a user-friendly interface.
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