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
This project helps businesses and researchers build, test, and deploy systems that suggest products, content, or services to users. It takes in historical user interaction data and outputs personalized recommendations, which can be integrated into websites, apps, or internal tools. Anyone involved in enhancing user experience through tailored suggestions, such as e-commerce managers, content strategists, or product owners, would find this useful.
About elliot
sisinflab/elliot
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
For researchers working on recommender systems, this project helps evaluate and compare different recommendation algorithms. You provide your dataset and a configuration file outlining the experimental setup, and it produces a detailed report on model performance, including various metrics, statistical analyses, and optimized hyperparameters. It's designed for academics and data scientists focused on rigorous evaluation of recommendation models.
Related comparisons
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