Microsoft Recommenders

Microsoft Recommenders is an open-source repository providing examples and best practices for building recommendation systems, developed by Microsoft's AI and Research division. With over 19,000 stars as of 2026, it serves as a comprehensive toolkit for data scientists and engineers implementing recommendation engines in production. Key features include: algorithm implementations (classical collaborative filtering including ALS, BPR, SAR, and SVD; deep learning models including Neural Collaborative Filtering (NCF), Wide and Deep, DeepFM, DKN, and xDeepFM; sequential and session-based models including SASRec, BERT4Rec, GRU4Rec, and SLi-Rec; and multi-task and multi-objective models), Azure integration (Azure Machine Learning, Azure Databricks, and Azure Synapse pipelines for distributed training and deployment), evaluation metrics (precision@k, recall@k, NDCG, MAP, MRR, AUC, and GAUC with proper train-test split strategies), feature engineering (user and item features, categorical encoding, embedding techniques, and feature crossing), distributed training (PySpark-based implementations for large-scale data using Azure Databricks and Synapse), GPU training (PyTorch and TensorFlow implementations with GPU support), model serving (deployment to Azure Kubernetes Service, Azure Container Instances, and real-time scoring endpoints), data preparation utilities ( negative sampling, data splitting by time or random, feature engineering helpers), notebooks (over 30 Jupyter notebooks covering data understanding, model training, evaluation, and deployment for each algorithm), and operationalization (production-ready pipelines with CI/CD, monitoring, and A/B testing guidance).