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: Fits a `user`-`item` pair and a real-valued target `y`.
  
  Parameters
  ----------
  user
      A user ID.
  item
      An item ID.
  y
      Reward feedback from the user for the item. This may be a boolean or a number.
  x
      Optional context to use.
docstr_tok: ["Fits","a","`","user","`","-","`","item","`","pair","and","a","real-valued","target","`","y","`",".","Parameters","--","--","--","--","--","user","A","user","ID",".","item","An","item","ID",".","y","Reward","feedback","from","the","user","for","the","item",".","This","may","be","a","boolean","or","a","number",".","x","Optional","context","to","use","."]
code: def learn_one(
      self, user: ID, item: ID, y: Reward, x: dict | None = None
  ):
      """Fits a `user`-`item` pair and a real-valued target `y`.
  
      Parameters
      ----------
      user
          A user ID.
      item
          An item ID.
      y
          Reward feedback from the user for the item. This may be a boolean or a number.
      x
          Optional context to use.
  
      """
  
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 3 for calling the Ranker obj's underlying member method code with example usage: obj.learn_one(user, item, y, x) without return types