https://recsyswiki.com/api.php?action=feedcontributions&feedformat=atom&user=Ls13cstudo 2026-05-11T16:36:39Z User contributions MediaWiki 1.34.2 https://recsyswiki.com/index.php?title=Recommender101&diff=2351 2015-11-23T10:03:08Z

<p>Ls13cstudo: </p> <hr /> <div>'''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.<br /> <br /> Implemented '''recommender algorithms''' include among others<br /> * [[KNN|nearest neighbors (kNN)]], <br /> * [[SlopeOne]], <br /> * [[matrix factorization]] methods, e.g., [[FunkSVD]], Koren's [[Asymmetric SVD]] and [[SVD++]],<br /> * [[BPR|Bayesian Personalized Ranking]], <br /> * [[Factorization Machines]],<br /> * [[content-based filtering]].<br /> <br /> Recommender algorithms can be evaluated with the help of cross-validation and '''accuracy metrics''' including <br /> * [[Precision]], <br /> * [[Recall]], <br /> * [[NDCG]],<br /> * [[MAE]],<br /> * [[RMSE]], <br /> * [[AUC]].<br /> <br /> Additional metrics can be used to measure recommendation '''biases''', e.g., <br /> * aggregate [[diversity]],<br /> * and the [[Gini index]].<br /> <br /> == External links ==<br /> * [http://ls13-www.cs.tu-dortmund.de/homepage/recommender101 Recommender101 Homepage]<br /> <br /> [[Category: Java]]<br /> [[Category: Software]]</div>

Ls13cstudo https://recsyswiki.com/index.php?title=Recommender101&diff=2285 2015-04-30T08:41:45Z

<p>Ls13cstudo: Added categories</p> <hr /> <div>'''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.<br /> <br /> Implemented algorithms: Nearest neighbors (kNN), SlopeOne, matrix factorization methods, BPR, content-based filtering and others<br /> <br /> Evaluation techniques: Cross-validation; metrics include Precision, Recall, NDCG, MAE, RMSE, AUC, Gini index and others<br /> <br /> == External links ==<br /> * [http://ls13-www.cs.tu-dortmund.de/homepage/recommender101 Recommender101 Homepage]<br /> <br /> [[Category: Java]]<br /> [[Category: Software]]</div>

Ls13cstudo https://recsyswiki.com/index.php?title=Recommender101&diff=2284 2015-04-30T08:40:10Z

<p>Ls13cstudo: Initial creation</p> <hr /> <div>'''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.<br /> <br /> Implemented algorithms: Nearest neighbors (kNN), SlopeOne, matrix factorization methods, BPR, content-based filtering and others<br /> <br /> Evaluation techniques: Cross-validation; metrics include Precision, Recall, NDCG, MAE, RMSE, AUC, Gini index and others<br /> <br /> == External links ==<br /> * [http://ls13-www.cs.tu-dortmund.de/homepage/recommender101 Recommender101 Homepage]</div>

Ls13cstudo