This week we released the latest version of librec-auto, a tool for automating batch-style recommender systems experiments. The GitHub repository is ( See also the documentation.

Among the new features available in this version are:

  • Fairness-aware recommendation support: Version 0.2 has a variety of features to support experimentation with fairness in recommendation including fairness metrics and re-ranker support.
  • Bayesian black-box optimization: This release contains a preliminary implementation of black-box parameter optimization for hyperparameters using the hyperopt library.
  • Setup wizard: A simple tool to help create new experimental studies, working from a data file supplied by the user.
  • Saved split files: Cross-validation splits are now saved for debugging, reference and for the computation of statistics over training or test sets.
  • Enhanced support for re-ranking: librec-auto has a number of built-in re-ranking algorithms for fairness-aware and diversity-aware recommendation, plus support for custom re-rankers.
  • Python-side metrics: LibRec has a substantial library of recommendation metrics. However, it is now possible for experiments to implement their own metrics in Python and integrate them with recommendation experiments.

librec-auto can be installed using pip:

pip install librec-auto

You can also clone the source repository:

git clone
cd librec-auto
python install

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