[...] technique allows us to represent product or user as low dimensional continuous vectors, while the one-hot encoding method will lead to the curse of dimensionality for the machine learning models
Answer
embedding
status
not learned
measured difficulty
37% [default]
last interval [days]
repetition number in this series
0
memorised on
scheduled repetition
scheduled repetition interval
last repetition or drill
Parent (intermediate) annotation
Open it embedding technique allows us to represent product or user as low dimensional continuous vectors, while the one-hot encoding method will lead to the curse of dimensionality for the machine learni