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Ranker

Base class for ranking models.

Parameters

  • seed (int) – defaults to None

    Random number generation seed. Set this for reproducibility.

Attributes

  • is_contextual

Methods

clone

Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.

learn_one

Fits a user-item pair and a real-valued target y.

Parameters

  • user (Union[str, int])
  • item (Union[str, int])
  • y (Union[numbers.Number, bool])
  • x (dict) – defaults to None
predict_one

Predicts the target value of a set of features x.

Parameters

  • user (Union[str, int])
  • item (Union[str, int])
  • x (dict) – defaults to None

Returns

typing.Union[numbers.Number, bool]: The predicted preference from the user for the item.

rank

Rank models by decreasing order of preference for a given user.

Parameters

  • user (Union[str, int])
  • items (Set[Union[str, int]])
  • x (dict) – defaults to None