Recommender101

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 the user to implement own recommenders and metrics.

Implemented algorithms: Nearest neighbors (kNN), SlopeOne, matrix factorization methods, BPR, content-based filtering and others

Evaluation techniques: Cross-validation; metrics include Precision, Recall, NDCG, MAE, RMSE, AUC, Gini index and others

External links

• Recommender101 Homepage