class_docstr: Base class for ranking models.
  
  Parameters
  ----------
  seed
      Random number generation seed. Set this for reproducibility.
class_docstr_tok: ["Base","class","for","ranking","models",".","Parameters","--","--","--","--","--","seed","Random","number","generation","seed",".","Set","this","for","reproducibility","."]
docstr: Predicts the target value of a set of features `x`.
  
  Parameters
  ----------
  user
      A user ID.
  item
      An item ID.
  x
      Optional context to use.
  
  Returns
  -------
  The predicted preference from the user for the item.
docstr_tok: ["Predicts","the","target","value","of","a","set","of","features","`","x","`",".","Parameters","--","--","--","--","--","user","A","user","ID",".","item","An","item","ID",".","x","Optional","context","to","use",".","Returns","--","--","--","-","The","predicted","preference","from","the","user","for","the","item","."]
code: def predict_one(
      self, user: ID, item: ID, x: dict | None = None
  ) -> Reward:
      """Predicts the target value of a set of features `x`.
  
      Parameters
      ----------
      user
          A user ID.
      item
          An item ID.
      x
          Optional context to use.
  
      Returns
      -------
      The predicted preference from the user for the item.
  
      """
  
file_path: river/river/reco/base.py
imports: from __future__ import annotations
  import abc
  import numbers
  import random
  import typing
  from river import base
label_desc: Use image node_id 4 for calling the Ranker obj's underlying member method code with example usage: obj.predict_one(user, item, x) without return types