Ecommerce-product-recommendation-system and E-Commerce-Product-Recommendation
These are **competitors** — both are standalone machine learning-based recommendation systems that independently solve the same problem of generating personalized product suggestions for e-commerce customers using collaborative filtering and popularity metrics, with no technical dependency or integration between them.
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-Product-Recommendation
sanjeebtiwary/E-Commerce-Product-Recommendation
E-commerce businesses are always striving to provide personalized experiences to their customers to increase engagement and loyalty. One way to achieve this is through product recommendation systems. In this project, we will build a recommendation system for an e-commerce website using batch processing and stream processing techniques.
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