Difference between revisions of "Recommender101"
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| − | '''Recommender101''' is a lightweight and easy-to-use framework written in Java to carry out offline experiments for Recommender Systems | + | '''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 | + | 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 | + | Evaluation techniques: Cross-validation; metrics include [[Precision]], [[Recall]], [[NDCG]], [[MAE]], [[RMSE]], [[AUC]], [[Gini index]] and others |
== External links == | == External links == | ||
Revision as of 06:22, 31 July 2015
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
