class_docstr: Generic class for a recommender model. All recommendation models should inherit from this class.
  
  Parameters
  ----------------
  name: str, required
      Name of the recommender model.
  
  trainable: boolean, optional, default: True
      When False, the model is not trainable.
  
  verbose: boolean, optional, default: False
      When True, running logs are displayed.
  
  Attributes
  ----------
  num_users: int
      Number of users in training data.
  
  num_items: int
      Number of items in training data.
  
  total_users: int
      Number of users in training, validation, and test data.
      In other words, this includes unknown/unseen users.
  
  total_items: int
      Number of items in training, validation, and test data.
      In other words, this includes unknown/unseen items.
  
  uid_map: int
      Global mapping of user ID-index.
  
  iid_map: int
      Global mapping of item ID-index.
  
  max_rating: float
      Maximum value among the rating observations.
  
  min_rating: float
      Minimum value among the rating observations.
  
  global_mean: float
      Average value over the rating observations.
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docstr: Generate top-K item recommendations for a given user. Key difference between
  this function and rank() function is that rank() function works with mapped
  user/item index while this function works with original user/item ID. This helps
  hide the abstraction of ID-index mapping, and make model usage and deployment cleaner.
  
  Parameters
  ----------
  user_id: str, required
      The original ID of the user.
  
  k: int, optional, default=-1
      Cut-off length for recommendations, k=-1 will return ranked list of all items.
  
  remove_seen: bool, optional, default: False
      Remove seen/known items during training and validation from output recommendations.
  
  train_set: :obj:`cornac.data.Dataset`, optional, default: None
      Training dataset needs to be provided in order to remove seen items.
  
  Returns
  -------
  recommendations: list
      Recommended items in the form of their original IDs.
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code: def recommend(self, user_id, k=-1, remove_seen=False, train_set=None):
      """Generate top-K item recommendations for a given user. Key difference between
      this function and rank() function is that rank() function works with mapped
      user/item index while this function works with original user/item ID. This helps
      hide the abstraction of ID-index mapping, and make model usage and deployment cleaner.
  
      Parameters
      ----------
      user_id: str, required
          The original ID of the user.
  
      k: int, optional, default=-1
          Cut-off length for recommendations, k=-1 will return ranked list of all items.
  
      remove_seen: bool, optional, default: False
          Remove seen/known items during training and validation from output recommendations.
  
      train_set: :obj:`cornac.data.Dataset`, optional, default: None
          Training dataset needs to be provided in order to remove seen items.
  
      Returns
      -------
      recommendations: list
          Recommended items in the form of their original IDs.
      """
      user_idx = self.uid_map.get(user_id, -1)
      if user_idx == -1:
          raise ValueError(f"{user_id} is unknown to the model.")
  
      if k < -1 or k > self.total_items:
          raise ValueError(
              f"k={k} is invalid, there are {self.total_users} users in total."
          )
  
      item_indices = np.arange(self.total_items)
      if remove_seen:
          seen_mask = np.zeros(len(item_indices), dtype="bool")
          if train_set is None:
              raise ValueError(
                  "train_set must be provided to remove seen items."
              )
          if user_idx < train_set.csr_matrix.shape[0]:
              seen_mask[
                  train_set.csr_matrix.getrow(user_idx).indices
              ] = True
              item_indices = item_indices[~seen_mask]
  
      item_rank, _ = self.rank(user_idx, item_indices)
      if k != -1:
          item_rank = item_rank[:k]
  
      recommendations = [self.item_ids[i] for i in item_rank]
      return recommendations
  
imports: import copy
  import inspect
  import os
  import pickle
  import warnings
  from datetime import datetime
  from glob import glob
  import numpy
  from ..exception import ScoreException
  from ..utils.common import clip
label_desc: Use image node_id 22 for calling the Recommender obj's underlying member method code with example usage: obj.recommend(user_id, k, remove_seen, train_set) and returns: recommendations