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```{r setup, include=FALSE}
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## 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)
```
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