recommenders and Reco-papers
A comprehensive software library for building and evaluating recommendation systems complements a curated collection of foundational research papers, as practitioners typically implement algorithms informed by the academic literature.
About recommenders
recommenders-team/recommenders
Best Practices on Recommendation Systems
Provides implementations of classical and deep learning algorithms (ALS, xDeepFM, DKN, sequential models) alongside Jupyter notebooks covering the full recommendation pipeline: data preparation, model training, offline evaluation, hyperparameter optimization, and Azure deployment. Includes utility functions for dataset loading, metric computation, and train/test splitting across multiple backends (CPU, GPU, PySpark), supporting both collaborative filtering and content-based approaches.
About Reco-papers
wzhe06/Reco-papers
Classic papers and resources on recommendation
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