Not convinced their user embedding creation is useful. Did not read in detail but it seems to use a list of edits similar to how one may create paragraph vectors as an average of word vectors. But if I had to guess, they're not really capturing more information than they originally had with a one hot vector of whether or not a user had edited a specific article. It would have been better if they had bench marked against this. I would wager that a simple random forest and the one hot vector would do just as well if not better than their NN solution.
Not convinced their user embedding creation is useful. Did not read in detail but it seems to use a list of edits similar to how one may create paragraph vectors as an average of word vectors. But if I had to guess, they're not really capturing more information than they originally had with a one hot vector of whether or not a user had edited a specific article. It would have been better if they had bench marked against this. I would wager that a simple random forest and the one hot vector would do just as well if not better than their NN solution.