Vaibhav67979/Ecommerce-product-recommendation-system

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.

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