code: def create_weighted_matrix(self):
      for ts in self.train_set_list:
          weighted_matrix = np.ones((len(self.users), len(self.users)))
  
          for u in range(len(self.users)):
              user_u = self.user_id_to_user[u]
              nu = ts["items_seen_by_user"].get(user_u, [])
              if nu:
                  for v in range(u, len(self.users)):
                      if u == v:
                          nuv = len(nu)
                      else:
                          user_v = self.user_id_to_user[v]
                          nv = ts["items_seen_by_user"].get(user_v, [])
  
                          # nuv = len(set(nu).intersection(nv)) / (len(nu) + len(nv))
                          nuv = 1 / (len(nu) + len(nv))
  
                      alpha = nuv if nuv != 0 else 1
                      weighted_matrix[u][v] = alpha
                      weighted_matrix[v][u] = alpha
          self.weighted_matrices.append(weighted_matrix)
  
imports: from scipy.spatial.distance import squareform, pdist
  import numpy
  import os
  from caserec.clustering.kmedoids import kmedoids
  from caserec.recommenders.item_recommendation.base_item_recommendation import BaseItemRecommendation
  from caserec.recommenders.item_recommendation.itemknn import ItemKNN
  from caserec.recommenders.item_recommendation.most_popular import MostPopular
  from caserec.recommenders.item_recommendation.userknn import UserKNN
  from caserec.utils.process_data import ReadFile, WriteFile
  from caserec.recommenders.item_recommendation.bprmf import BprMF
label_desc: Use image node_id 4 for calling the GroupBasedRecommender obj's underlying member method code with example usage: obj.create_weighted_matrix() without return types