Calculates evaluation metrics for implicitfeedback recommender systems that are based on lowrank matrix factorization models, given the fitted model matrices and data, thus allowing to compare models from a variety of libraries. Metrics include P@K (precisionatk, for topK 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), ROCAUC (area under the receiveroperating characteristic curve), and PRAUC (area under the precisionrecall curve). These are calculated on a peruser basis according to the ranking of items induced by the model, using efficient multithreaded routines. Also provides functions for creating traintest splits for model fitting and evaluation.
Package details 


Author  David Cortes 
Maintainer  David Cortes <david.cortes.rivera@gmail.com> 
License  BSD_2_clause + file LICENSE 
Version  0.1.3 
URL  https://github.com/davidcortes/recometrics 
Package repository  View on CRAN 
Installation 
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