NCF Recommender- The target encoded within the model input, why doesn't it overfit easily?
The input of the system is a given user and a given item (each respectively one-hot encoded). The output of the system is binary (picked or not).

The authors filtered the data to require each user to have picked at least 20 different items. Thus, enabling the system to learn the general preferences of a user.

The system could overfit. The system could memorize that users would only pick items they have already picked. 

The system would fail on the chosen evaluation protocol - held-out last interaction. The model performs better than other recommendation systems on this evaluation protocol which is evidence that this system is not overfitting.