Rating scorer that predicts a constant rating for all items.
Item scorer that combines a primary scorer with a baseline.
Rating scorer that predicts the global mean rating for all items.
A default builder used to create GlobalMeanPredictors.
Rating scorer that returns the item's mean rating for all predictions.
A builder to create ItemMeanPredictors.
Baseline scorer using least-squares estimates of preferences, trained by gradient descent.
The builder for the least squares predictor.
Rating scorer that returns the user's average rating for all predictions.
Enum expressing where a score came from in recommender that uses a baseline fallback.
Annotation for the baseline scorer of a stacked item scorer, or an item scorer used as a baseline in another component.
Parameter: the value used by the constant scorer.
Damping parameter for means in baseline predictors.
The primary scorer for a
Baseline scores for user mean ratings.
Baseline predictors are like rating predictors, but they provide an unboxed
SparseVector-based interface and are
guaranteed to be able to predict for all users and items. They are used for
things like normalizations and starting points for iterative methods.