recommenders and elliot
These are complements: Recommenders provides best-practice implementations of recommendation algorithms and end-to-end pipelines, while Elliot provides a specialized evaluation framework for rigorously benchmarking and comparing recommender systems, making them naturally used together in a development and validation workflow.
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 elliot
sisinflab/elliot
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
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