class_docstr: Neural Collaborative Filtering (NCF) implementation
  
  :Citation:
  
      He, Xiangnan, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. "Neural collaborative filtering."
      In Proceedings of the 26th International Conference on World Wide Web, pp. 173-182. International World Wide Web
      Conferences Steering Committee, 2017. Link: https://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf
class_docstr_tok: ["Neural","Collaborative","Filtering","(","NCF",")","implementation",":","Citation",":","He",",","Xiangnan",",","Lizi","Liao",",","Hanwang","Zhang",",","Liqiang","Nie",",","Xia","Hu",",","and","Tat-Seng","Chua",".","``","Neural","collaborative","filtering",".","''","In","Proceedings","of","the","26th","International","Conference","on","World","Wide","Web",",","pp",".","173-182.","International","World","Wide","Web","Conferences","Steering","Committee",",","2017",".","Link",":","https",":","\/\/www.comp.nus.edu.sg\/~xiangnan\/papers\/ncf.pdf"]
code: def _predict(self, user_input, item_input):
      # index converting
      user_input = np.array([self.user2id[x] for x in user_input])
      item_input = np.array([self.item2id[x] for x in item_input])
  
      # get feed dict
      feed_dict = {
          self.user_input: user_input[..., None],
          self.item_input: item_input[..., None],
      }
  
      # calculate predicted score
      return self.sess.run(self.output, feed_dict)
  
file_path: recommenders/recommenders/models/ncf/ncf_singlenode.py
imports: import os
  import numpy
  import tensorflow
  import tf_slim
  from time import time
  import logging
label_desc: Use image node_id 8 for calling the NCF obj's underlying member method code with example usage: obj._predict(user_input, item_input) and returns: self