A Python library providing evaluation metrics and diagnostic tools for recommender systems.
recmetrics is a Python library that provides a collection of evaluation metrics and diagnostic tools specifically for recommender systems. It helps data scientists and machine learning engineers measure the performance, coverage, novelty, and personalization of recommendation algorithms to build more effective and unbiased systems.
Data scientists, machine learning engineers, and researchers working on recommender systems who need standardized metrics to evaluate model performance and diagnose issues like popularity bias or low coverage.
Developers choose recmetrics because it consolidates essential recommender system metrics into one open-source Python library, saving time from manual implementation and offering practical visualizations for model diagnostics directly informed by real-world industry use.
A library of metrics for evaluating recommender systems
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Consolidates essential recommender metrics like MAP@K, coverage, novelty, and personalization into one library, saving time from manual implementation, as shown in the detailed README examples.
Includes ready-to-use plots such as long tail plots and ROC curves, directly aiding model diagnosis, with examples provided in the example.ipynb notebook.
Built from practical data science experience in retail, ensuring metrics are relevant for real-world applications, as stated in the philosophy section.
The maintainer actively encourages contributions and provides direct contact for issues, making it responsive to user needs, as highlighted in the README header.
The README admits 'Full documentation coming soon,' forcing users to rely solely on a single example notebook, which can hinder onboarding and advanced usage.
Lacks some modern metrics like NDCG for ranking or fairness evaluations, which are increasingly important in production systems, compared to more comprehensive libraries.
As a community-driven project, some functions may have inconsistencies or bugs, as noted in the call for issue submissions, requiring users to verify outputs carefully.