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: Recommend k number of items similar to the item of interest.
  
  Args:
      df_clean (pandas.DataFrame): Dataframe with cleaned text.
      k (int): Number of recommendations to return.
  
  Returns:
      pandas.DataFrame: Dataframe containing id of top k recommendations for all items.
docstr_tok: ["Recommend","k","number","of","items","similar","to","the","item","of","interest",".","Args",":","df_clean","(","pandas.DataFrame",")",":","Dataframe","with","cleaned","text",".","k","(","int",")",":","Number","of","recommendations","to","return",".","Returns",":","pandas.DataFrame",":","Dataframe","containing","id","of","top","k","recommendations","for","all","items","."]
code: def recommend_top_k_items(self, df_clean, k=5):
      """Recommend k number of items similar to the item of interest.
  
      Args:
          df_clean (pandas.DataFrame): Dataframe with cleaned text.
          k (int): Number of recommendations to return.
  
      Returns:
          pandas.DataFrame: Dataframe containing id of top k recommendations for all items.
      """
      if k > len(df_clean) - 1:
          raise ValueError(
              "Cannot get more recommendations than there are items. Set k lower."
          )
      self.__create_full_recommendation_dictionary(df_clean)
      self.__organize_results_as_tabular(df_clean, k)
  
      return self.top_k_recommendations
  
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 10 for calling the TfidfRecommender obj's underlying member method code with example usage: obj.recommend_top_k_items(df_clean, k) and returns: self