While we use the loss function commonly used for binary classification during training, we ultimately want to frame our question as a ranking problem.
after iterating through the initial observations, we compute the mean average precision (MAP) over the remaining (unseen) ingredients/questions and compare it to the MAP of the baseline RNN model on the same unseen examples.