Recommender system extension contains operators that are suited for typical recommendation tasks: filtering information for the particular user via :

- Predicting most-probable ratings for the items of some DMR (rating predictions task)
- Ranking non-seen items by the user according to estimated (Top-N Items task)

Besides providing operators that return predictions based on user-item preference data, extension includes specific operators enabling model application and evaluation, and is accompanied with utility tools like dataset meta-data extraction operator and workflows enabling large scale experiments for optimizing performance trade-off for specific problems.