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Recommender101 is a lightweight and easy-to-use framework written in Java to carry out offline experiments for Recommender Systems. It provides the user with various metrics and common evaluation strategies as well as some example recommenders and a dataset. The framework is easily extensible and allows users to quickly implement their own recommenders and metrics. On the other hand, users who only want to test pre-implemented algorithms can instantly launch the software via Ant or Eclipse.

Implemented recommender algorithms include among others

Recommender algorithms can be evaluated with the help of cross-validation and accuracy metrics including

Additional metrics can be used to measure recommendation biases, e.g.,

External links