A comprehensive guide to recommender systems—from collaborative filtering fundamentals to neural networks, graph-based methods, and production deployment at scale.
What you'll learn: Build modern recommendation engines from scratch. Master collaborative filtering, matrix factorization, deep learning architectures (NCF, transformers, GNNs), contextual bandits, causal debiasing, and production system design. Real implementations with code and best practices.
Types of recommender systems, key concepts, and real-world applications across industries.
User-based and item-based collaborative filtering, similarity metrics, and handling sparse data.
Feature extraction, TF-IDF, user profiles, and building content-based recommenders.
Singular Value Decomposition, low-rank approximations, and latent factor models.
Bayesian Personalized Ranking, implicit feedback signals, and handling missing data.
Multi-layer perceptrons for user-item interactions, embedding layers, and NCF architecture.
Factorization Machines, DeepFM, xDeepFM for feature interactions and CTR prediction.
RNNs, GRU4Rec, SASRec, and transformer-based recommenders for sequential patterns.
Graph convolutions, LightGCN, PinSage, and leveraging user-item graph structure.
Dual encoders, retrieval-ranking pipelines, and multi-objective optimization.
Multi-armed bandits, contextual bandits, Thompson Sampling, and exploration-exploitation tradeoff.
Debiasing recommenders, causal inference for recommendations, and counterfactual evaluation.
Precision@K, NDCG, MAP, diversity metrics, and online A/B testing for recommender systems.
Candidate generation, ranking, retrieval systems, and serving recommendations at scale.
Fairness in recommendations, filter bubbles, bias mitigation, and solving the cold start problem.
This series covers both theory and practice, with implementations in Python using PyTorch, TensorFlow, and libraries like Surprise, LightFM, and RecBole. Each article includes code examples and exercises.