Evaluating Rating Predictions

While rating prediction is no longer a widely-studied recommendation task, LensKit provides support for evaluating predictions for completeness and reproducing or comparing against historical research.

LensKit provides two prediction accuracy metrics: RMSE() and MAE(). They support both global (micro-averaged) and per-user (macro-averaged) computation.

Changed in version 2025.1: The prediction accuracy metric interface has changed to use item lists.

Calling Metrics

There are two ways to directly call a prediction accuracy metric:

• Pass two item lists, the first containing predicted ratings (as the list’s scores()) and the second containing ground-truth ratings as a rating field.

• Pass a single item list with scores and a rating field.

For evaluation, you will usually want to use RunAnalysis, which takes care of calling the prediction metric for you.

Missing Data

There are two important missing data cases for evaluating predictions:

• Missing predictions (the test data has a rating for which the system could not generate a prediction).

• Missing ratings (the system generated a prediction with no corresponding test rating).

By default, LensKit throws an error in both of these cases, to help you catch bad configurations. We recommend using a fallback predictor, such setting up FallbackScorer with BiasScorer, when measuring rating predictions to ensure that all items are scored. The alternative design — ignoring missing predictions — means that different scorers may be evaluated on different items, and a scorer perform exceptionally well by only scoring items with high confidence.

Note

Both topn_pipeline() and predict_pipeline() default to using a BiasScorer() as a fallback when rating prediction is enabled.

If you want to skip missing predictions, pass missing_scores="ignore" to the metric function:

RMSE(user_preds, user_ratings, missing_scores="ignore")

The corresponding missing_truth="ignore" will cause the metric to ignore predictions with no corresponding rating (this case is unlikely to produce unhelpful eval results, but may indicate a misconfiguration in how you determine the items to score).