evalChart

evaluation results, class <code>"evalRecResults"</code>.

class <code>"character"</code>, is the variable that will be shown on the "x" axis. Possible values are: <code>"items"</code>, <code>"users"</code>.

class <code>"character"</code>, is the variable that will be shown on the "y" axis. Possible values are: <code>"num_of_ratings"</code>, <code>"%_of_ratings"</code>.

class <code>"character"</code>, the label to be printed on the "x" axes.

x_label

class <code>"character"</code>, the label to be printed on the "y" axes.

y_label

class <code>"numeric"</code>, scale of the "y" axes.

y_lim

This method visualizes data characteristics on a two dimensional graph, where "x" axes shows either items ordered by descending popularity, or users based on the number of ratings they have submitted. Moreover the "y" axes shows the number of ratings.

Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.

evalChart: Visualization of data characteristics.

Description

Usage

Arguments

Value

See Also