class_docstr: Recommend most popular items of the current session.
  
  Parameters
  ----------
  name: string, default: 'SPop'
      The name of the recommender model.
  
  use_session_popularity: boolean, optional, default: True
      When False, no item frequency from history items in current session are being used.
  
  References
  ----------
  Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk:
  Session-based Recommendations with Recurrent Neural Networks, ICLR 2016
code: def score(self, user_idx, history_items, **kwargs):
      item_scores = np.zeros(self.total_items, dtype=np.float32)
      max_item_freq = (
          max(self.item_freq.values()) if len(self.item_freq) > 0 else 1
      )
      for iid, freq in self.item_freq.items():
          item_scores[iid] = freq / max_item_freq
      if self.use_session_popularity:
          s_item_freq = Counter([iid for iid in history_items])
          for iid, cnt in s_item_freq.most_common():
              item_scores[iid] += cnt
      return item_scores
  
file_path: cornac/cornac/models/spop/recom_spop.py
imports: from collections import Counter
  import numpy
  from ..recommender import NextItemRecommender
label_desc: Use image node_id 3 for calling the SPop obj's underlying member method code with example usage: obj.score(user_idx, history_items) and returns: item_scores