Thanks, there is a little more information in my reply to abrichr above. But in short, there are a large number of predictions run on each piece of content, and

Thanks, there is a little more information in my reply to abrichr above. But in short, there are a large number of predictions run on each piece of content, and they each use different approaches - including traditional statistical models and ML, off-the-shelf APIs, as well as custom deep learning models we've developed through experimentation. So there's an evolving mix of models using different approaches (including CNNs, LSTM-like approaches / word2vec and some topic modelling using a doc2vec-style approach). It's still very much experimental though, and some predictions are way better than others! I'd love to do an arxiv.org paper or mlconf presentation down the track, as I think we have some interesting work here and I have an academic interest in the field, but it's way too early. In essence, we're working this out as we go to "make the thing" as practitioners first based on some unique insights, rather than it being a product derived from research first. [Edits for typos/clarity]