Personalized-Recommendation-Models

• Implemented various recommendation algorithms, starting from Baseline Estimates to Matrix Factorization.
• Fine-tuned Matrix Factorization with explicit feedback by adjusting hyperparameters, including latent dimensions, learning rate, and regularization weight.
• Implemented User-User Collaborative Filtering using Pearson Similarity.