Showing 3 of total 3 results (show query)
david-cortes
recometrics:Evaluation Metrics for Implicit-Feedback Recommender Systems
Calculates evaluation metrics for implicit-feedback recommender systems that are based on low-rank matrix factorization models, given the fitted model matrices and data, thus allowing to compare models from a variety of libraries. Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k), AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k), Hit@K (from which the 'Hit Rate' is calculated), RR@K (reciprocal rank at k, from which the 'MRR' or 'mean reciprocal rank' is calculated), ROC-AUC (area under the receiver-operating characteristic curve), and PR-AUC (area under the precision-recall curve). These are calculated on a per-user basis according to the ranking of items induced by the model, using efficient multi-threaded routines. Also provides functions for creating train-test splits for model fitting and evaluation.
Maintained by David Cortes. Last updated 3 months ago.
implicit-feedbackmatrix-factorizationrecommender-systemsopenblascppopenmp
28 stars 5.45 scoremhahsler
recommenderlabJester:Jester Dataset for 'recommenderlab'
Provides the Jester Dataset for package recommenderlab.
Maintained by Michael Hahsler. Last updated 3 years ago.
2.70 score 1 scriptsmhahsler
recommenderlabBX:Book-Crossing Dataset (BX) for 'recommenderlab'
Provides the Book-Crossing Dataset for the package recommenderlab.
Maintained by Michael Hahsler. Last updated 3 years ago.
2.70 score 1 scripts