Environment for Evaluating Recommender Systems
Item based model.
Normalized Discounted Cumulative Gain
Popularity based model.
Generate predictions.
Rank Score
Generate recommendation.
Create a recommender system.
Set stopping criteria.
Slope One model.
Dataset class for tuples (user, item, rating).
SVD model.
Item based model.
Weighted Alternating Least Squares based model.
Baseline algorithms exploiting global/item and user averages.
Bayesian Personalized Ranking based model.
Visualization of data characteristics.
Dataset class.
Define dataset.
Dataset class.
Visualization of data characteristics.
Evaluation model.
Creating the evaluation model.
Evaluates the requested prediction algorithm.
Evaluates the requested recommendation algorithm.
Evaluation results.
Returns the Area under the ROC curve.
Ratings histogram.
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.