```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` ## rrecsys This is a package in R that provides implementations of several baselines (Item/User Average and Most Popular Item Recommendation) and other well-known recommendation algorithms. In particular, two main families of recommendation algorithms (i.e., Collaborative filtering and Matrix factorization) are implemented, as shown in the following: 1. Collaborative filtering * Weighted Slope One * User-based k-nearest neighbour * Item-based k-nearest neighbour 2. Matrix factorization * Simon Funk's SVD with stochastic gradient descent * weighted Alternated Least Squares (wALS) * Bayesian Personalized Ranking (BPR) rrecsys addresses the two most common scenarios in Recommender Systems: * Rating Prediction (e.g. on a scale of 1 to 5 stars), and * Item Recommendations (e.g. a list of top-N recommended items). All algorithms can run on a user-item rating matrix that holds data of either item ratings (e.g., 1-5 rating scale) or item purchases/views (e.g., purchased item or not purchased item). The package offers as well an evaluation methodology with the following standard metrics for the specific task: * Rating Prediction task: global or user-based MAE and RMSE * Item Recommendation task: precision, recall, F1, NDCG, rank score and all the elements of the confusion matrix. ## Installation & Loading the package The package is available on CRAN and as well on [GitHub](https://github.com/ludovikcoba). To install it from CRAN: ```{r, eval=FALSE} install.packages("rrecsys") ``` Once the package is installed it can be loaded it in the environment: ```{r, eval=FALSE} library(rrecsys) ```