While the recommenderlab library is super popular in the R community, this is not the only choice for building a recommendation system. Here are some other popular libraries you may rely on to implement recommendation engines:

• rrecsys: There are several popular recommendation systems, such as Global/Item/User-Average baselines, Item-Based KNN, FunkSVD, BPR, and weighted ALS for rapid prototyping. Refer to https://cran.r-project.org/web/packages/rrecsys/index.htmlImplementations for more information.
• recosystem: The R wrapper of the libmf library (http://www.csie.ntu.edu.tw/~cjlin/libmf/) for recommender system using matrix factorization. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include collaborative filtering, matrix completion, and matrix recovery. High-performance multicore parallel computing is supported in this package.
• rectools: An advanced package for recommender systems to incorporate user and item covariate information, including item category preferences with parallel computation, novel variations on statistical latent factor model, focus group finder, NMF, ANOVA, and cosine models.