class_docstr: Compute baselines based on training information considering information about users and items
code: def compute_bi(self):
      # bi = (rui - mi - bu) / (regBi + number of interactions)
      self.bi = dict()
  
      for item in self.training_set["items"]:
          cont = 0
          for user in self.training_set["users_viewed_item"][item]:
              self.bi[item] = (
                  self.bi.get(item, 0)
                  + float(self.training_set["feedback"][user][item])
                  - self.training_set["mean_value"]
                  - self.bu.get(user, 0)
              )
              cont += 1
          if cont > 1:
              self.bi[item] = float(self.bi[item]) / float(10 + cont)
  
file_path: CaseRecommender/caserec/utils/extra_functions.py
imports: import sys
  import time
label_desc: Use image node_id 3 for calling the ComputeBui obj's underlying member method code with example usage: obj.compute_bi() without return types