class_docstr: Term Frequency - Inverse Document Frequency (TF-IDF) Recommender
  
  This class provides content-based recommendations using TF-IDF vectorization in combination with cosine similarity.
docstr: Create the full recommendation dictionary containing all recommendations for all items.
  
  Args:
      pandas.DataFrame: Dataframe with cleaned text.
docstr_tok: ["Create","the","full","recommendation","dictionary","containing","all","recommendations","for","all","items",".","Args",":","pandas.DataFrame",":","Dataframe","with","cleaned","text","."]
code: def __create_full_recommendation_dictionary(self, df_clean):
      """Create the full recommendation dictionary containing all recommendations for all items.
  
      Args:
          pandas.DataFrame: Dataframe with cleaned text.
      """
  
      # Similarity measure
      cosine_sim = linear_kernel(self.tfidf_matrix, self.tfidf_matrix)
  
      # sorted_idx has the indices that would sort the array.
      sorted_idx = np.argsort(cosine_sim, axis=1)
  
      data = list(df_clean[self.id_col].values)
      len_df_clean = len(df_clean)
  
      results = {}
      for idx, row in zip(range(0, len_df_clean), data):
          similar_indices = sorted_idx[idx][: -(len_df_clean + 1) : -1]
          similar_items = [
              (cosine_sim[idx][i], data[i]) for i in similar_indices
          ]
          results[row] = similar_items[1:]
  
      # Save to class
      self.recommendations = results
  
imports: from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.metrics.pairwise import linear_kernel
  from transformers import BertTokenizer
  import re
  import unicodedata
  import pandas
  import numpy
  import nltk
  from nltk.stem.porter import PorterStemmer
label_desc: Use image node_id 8 for calling the TfidfRecommender obj's underlying member method code with example usage: obj.__create_full_recommendation_dictionary(df_clean) without return types