#matrix-factorization

Build recommendation systems using collaborative filtering, content-based filtering, and hybrid approaches for personalization. Collaborative filtering approaches: 1. User-based CF: find similar users, recommend items liked by similar users, cosine similarity calculation. 2. Item-based CF: find similar items, recommend similar items to liked items, Pearson correlation. 3. Matrix factorization: SVD, NMF for dimensionality reduction, latent factor modeling. Content-based filtering: 1. Feature extraction: item attributes, TF-IDF for text features, categorical encoding. 2. Profile building: user preference vectors, weighted feature importance, learning user tastes. 3. Similarity computation: cosine similarity, Jaccard similarity, recommendation scoring. Deep learning approaches: 1. Neural Collaborative Filtering: user/item embeddings, deep neural networks, non-linear interactions. 2. Deep autoencoders: collaborative denoising, missing rating prediction, feature learning. 3. Recurrent neural networks: sequential recommendations, session-based filtering, temporal dynamics. Hybrid systems: 1. Weighted combination: linear combination of different approaches, weight optimization. 2. Mixed systems: present recommendations from different algorithms, user choice. 3. Cascade systems: hierarchical filtering, primary and secondary recommendation stages. Evaluation metrics: 1. Precision@K: relevant items in top-K recommendations, practical relevance measure. 2. Recall@K: coverage of relevant items, completeness assessment. 3. NDCG (Normalized Discounted Cumulative Gain): ranking quality, position-aware evaluation. Cold start problem: new user recommendations, new item recommendations, demographic-based initialization, content-based bootstrap, popularity-based fallback strategies.