Recommendation Systems
Build recommendation engines with collaborative filtering, content-based filtering, matrix factorization, and deep learning approaches. Types of Recommendation Systems Collaborative filtering recommends items based on user behavior similarity ("users who bought X also bought Y"). Content-based filtering recommends items similar to what the user has liked before (based on item features). Hybrid approaches combine both for better accuracy and cold-start handling. Collaborative Filtering User-based: find similar users and recommend what they liked. Item-based: find similar items to what the user purchased. Memory-based approaches compute similarity directly (cosine similarity, Pearson correlation). Model-based approaches use matrix factorization (SVD, NMF) to learn latent factors representing user preferences and item characteristics. Deep Learning for Recommendations Neural collaborative filtering (NCF) replaces dot products with neural networks for learning user-item interactions. Two-tower models encode users and items separately into embedding vectors, enabling fast retrieval with nearest neighbor search. Graph neural networks (GNNs) model user-item interactions as a bipartite graph, capturing higher-order relationships. Cold Start & Evaluation Cold start problem: new users or items have no interaction history. Solutions: use content-based features, ask for initial preferences, or recommend popular items. Evaluate with precision@k, recall@k, NDCG (Normalized Discounted Cumulative Gain), and Mean Average Precision. Online evaluation (A/B testing) is essential — offline metrics don't always correlate with user engagement.