Usage Examples

We provide extensive tutorials in Jupyter notebooks under the notebooks folder for guidelines on building recommenders, performing model selection, and evaluating performance:

• Data Overview provides an overview of data required to train recommender.

• Feature Engineering gives an overview of methods to create user and item features from structured, unstructured, and sequential data.

• Model Selection shows to do model selection by benchmarking recommenders using cross-validation.

• Evaluation benchmarks selected recommenders and baselines on test data with detailed evaluations.

• Advanced demonstrates some advanced functionality.