How to offer then recommendation based on the result?

[Jeriousman]

If I understood correctly, I see that pp in the result is the predicted probability of forgetting a word. So if pp is low, I should recommend a word associated with the pp as a user might have forgotten the word already. But from the result, I dont see associated word with pp. Then how can I recommend words to users? And again, is my saying correct? (words with low pp values should be recommended)

Finally, I checked roughly the result and compared p (ground truth) and pp (prediction) and seemed to find that p and pp are quite not matching when they should. For example, when p is 0.0001 pp is 0.9999 and when p is 0.9999, pp is 0.0001. I saw this case quite often when I was skimming. But MAE shows it is very similar as your paper which means training is not broken. Is it normal that p and app are not so matching often? Should I still recommend repeating lexeme according to the pp values?

[Jeriousman]

I guess I should just concat test set data with eval result and then find the lexeme?