docstr: Method to predict a rank for each user. In this implementation, for each unknown item, which will be
  predicted, we first look for its k most similar users and then take the intersection with the users that
  seen that item. Finally, the score of the unknown item will be the sum of the  similarities.
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code: def predict_similar_first_scores(
      self, user, user_id, unpredicted_items
  ):
      """
      Method to predict a rank for each user. In this implementation, for each unknown item, which will be
      predicted, we first look for its k most similar users and then take the intersection with the users that
      seen that item. Finally, the score of the unknown item will be the sum of the  similarities.
  
      """
  
      predictions = []
  
      # Select user neighbors, sorting user similarity vector. Returns a list with index of sorting values
      neighbors = sorted(
          range(len(self.su_matrix[user_id])),
          key=lambda m: -self.su_matrix[user_id][m],
      )
  
      for item_id in unpredicted_items:
          item = self.items[item_id]
          # Intersection bt. the neighbors closest to the user and the users who accessed the unknown item.
          common_users = list(
              set(self.users_id_viewed_item.get(item, [])).intersection(
                  neighbors[1 : self.k_neighbors]
              )
          )
  
          sim_sum = 0
          for user_v in common_users:
              sim_sum += self.su_matrix[user_id, user_v]
  
          predictions.append((user, item, sim_sum))
  
      return sorted(predictions, key=lambda x: -x[2])[
          : self.rank_length
      ]
  
file_path: CaseRecommender/caserec/recommenders/item_recommendation/userknn.py
imports: import numpy
  from caserec.recommenders.item_recommendation.base_item_recommendation import BaseItemRecommendation
  from caserec.utils.extra_functions import timed
label_desc: Use image node_id 5 for calling the UserKNN obj's underlying member method code with example usage: obj.predict_similar_first_scores(user, user_id, unpredicted_items) and returns: sorted