Learn how user-based and item-based collaborative filtering work from first principles - the math behind cosine similarity and Pearson correlation, how Amazon's item-to-item CF changed the industry, and how to build production-grade recommendation engines.
Learn how content-based filtering builds item feature vectors, constructs user profiles, and scores unseen items using TF-IDF and cosine similarity - no user overlap required.
How pointwise, pairwise, and listwise ranking approaches train models to produce the optimal ordering of items for search and recommendation.
Master matrix factorization for recommendations - SVD, Funk SVD, SGD and ALS optimization, biases, regularization, and implicit feedback with BPR. The algorithm that won the Netflix Prize.
Learn how modern recommendation engines work - from collaborative filtering and matrix factorization to neural two-tower models and learning to rank - as applied in production systems at Netflix, Amazon, and Spotify.
How deep learning revolutionized recommendations by replacing the linear dot product with learnable nonlinear interactions between users and items.
How to recommend to new users and new items when collaborative filtering has no interaction history - the cold start problem and its production solutions.
How two-tower neural networks enable billion-scale retrieval by learning separate user and item towers that can be precomputed for ultra-fast inference.
