recommenders

Why we included this project

Recommendation work usually means assembling a stack of separate libraries, but this project packages the whole pipeline in one place. It ships working implementations of classic and modern algorithms, from Alternating Least Squares and Bayesian Personalized Ranking to deep models like xDeepFM and sequential attention-based recommenders, alongside utilities for preparing data in the format each one expects, offline evaluation, and hyperparameter tuning. The notebooks walk through the math and code for each model, which makes it useful both as a learning reference and as a base to adapt for your own dataset, starting from the MovieLens quick-start example. Operationalization is covered too, with guidance for Spark and GPU environments and production deployment on Azure, so the path from prototype to a scaled service is laid out rather than left to guesswork.