recommenders and spotlight
The first tool provides a comprehensive framework and best practices for building recommendation systems, while the second offers a specialized library for implementing deep recommender models in PyTorch; therefore, they are complements, where the second can be a specific implementation within the broader guidance of the first.
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 spotlight
maciejkula/spotlight
Deep recommender models using PyTorch.
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