Important: Recommender101 has been deprecated. This repo is only for archiving the source code.
Recommender101 is a lightweight and easy-to-use framework written in Java to carry out offline experiments for Recommender Systems (RS). 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
Evaluation techniques: Cross-validation; metrics include Precision, Recall, NDCG, MAE, RMSE, AUC, Gini index and others