## 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

1. 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. To install it from CRAN:

    install.packages("rrecsys")

Once the package is installed it can be loaded it in the environment:

    library(rrecsys)