Ecommerce-product-recommendation-system and Product_Recommendation_Engine
These are competitors offering alternative implementations of similar recommendation approaches (collaborative filtering, content-based filtering, and hybrid methods) for the same e-commerce use case, where a user would select one based on code quality and maturity rather than use both 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 Product_Recommendation_Engine
atharvapathak/Product_Recommendation_Engine
Using algorithms such as collaborative filtering, content-based filtering, or hybrid methods, this recommendation engine offers personalized suggestions to users, enhancing their shopping or browsing experience.
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