From 7ae78c7d5f42e534424e936d288c329e8e4ca85d Mon Sep 17 00:00:00 2001 From: WANG Zhiwei <48282751+statwangz@users.noreply.github.com> Date: Thu, 18 May 2023 10:30:13 +0800 Subject: [PATCH] Update README --- README.Rmd | 2 +- README.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.Rmd b/README.Rmd index 8f5269b..88899a5 100644 --- a/README.Rmd +++ b/README.Rmd @@ -20,7 +20,7 @@ knitr::opts_chunk$set( [![R-CMD-check](https://github.com/YangLabHKUST/mfair/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/YangLabHKUST/mfair/actions/workflows/R-CMD-check.yaml) -Matrix factorization methods based on the paper [MFAI: A scalable Bayesian matrix factorization approach to leveraging auxiliary information](https://doi.org/10.48550/arXiv.2303.02566). +The R package `mfair` implements the methods based on the paper [MFAI: A scalable Bayesian matrix factorization approach to leveraging auxiliary information](https://doi.org/10.48550/arXiv.2303.02566). MFAI integrates gradient boosted trees in the probabilistic matrix factorization framework to effectively leverage auxiliary information. The parameters in MAFI can be automatically determined under the empirical Bayes framework, making it adaptive to the utilization of auxiliary information and immune to overfitting. diff --git a/README.md b/README.md index 92426f2..59923f2 100644 --- a/README.md +++ b/README.md @@ -10,8 +10,8 @@ coverage](https://codecov.io/gh/YangLabHKUST/mfair/branch/main/graph/badge.svg)] [![R-CMD-check](https://github.com/YangLabHKUST/mfair/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/YangLabHKUST/mfair/actions/workflows/R-CMD-check.yaml) -Matrix factorization methods based on the paper [MFAI: A scalable -Bayesian matrix factorization approach to leveraging auxiliary +The R package `mfair` implements the methods based on the paper [MFAI: A +scalable Bayesian matrix factorization approach to leveraging auxiliary information](https://doi.org/10.48550/arXiv.2303.02566). MFAI integrates gradient boosted trees in the probabilistic matrix factorization framework to effectively leverage auxiliary information. The parameters