Recommendation engines have a number of different applications. From books to movies, they enable the analysis and prediction of consumer preferences. The prevalence of recommender systems in both the business and computational world has led to clear advances in prediction models over the past years. Current R packages include recosystem and recommenderlab. However, our new package, rectools, currently under development, extends its capabilities in several directions. One of the most important differences is that rectools allows users to incorporate covariates, such as age and gender, to improve predictive ability and better understand consumer behavior. Our software incorporates a number of different methods, such as non-negative matrix factorization, random effects models, and nearest neighbor methods. In addition to our incorporation of covariate capabilities, rectools also integrates several kinds of parallel computation. Examples of real data will be presented, and results of computational speedup experiments will be reported; results so far have been very encouraging. Code is being made available on GitHub, at https://github.com/Pooja-Rajkumar/rectools.
