> You can do the same thing with comments that he claims TT does with video (dwell time threshold feature) >I don't work at Twitter, but I doubt that matters v

> You can do the same thing with comments that he claims TT does with video (dwell time threshold feature) >I don't work at Twitter, but I doubt that matters very much. >Also regarding "negative sentiment" feedback from users. TikTok does not have a UI tool to say "I don't like this", but other apps do. For example, you can ask Instagram to "show me fewer posts like this". There's a big difference in the usefulness of implicit and explicit feedback for recommenders, and there's a long history of research trying to address the shortcomings of each, or combine them to avoid their weaknesses (e.g. research into presentation biases, augmented matrix factorization, MF with implicit data, ensembles) Implicit feedback (dwell times etc) are particularly prone to noise and biases. On the other hand, the amount of explicit data (likes, downvotes) companies can get is so small compared to implicit data, that many companies don't even bother with their explicit data. E.g. even YouTube claim not to use likes/dislikes, in order to better recommend to the long tail of items/users. So it very much does matter, being able to get good implicit data. The interesting thing implied by OP is that TikTok seems to have completely sidestepped the pitfalls of each form of data. They in essence seem to have made implicit data be a gold standard judgment of relevance, which is really cool I think. > I don't want to sound overly dismissive, but it's pretty clear reading this that the author has not worked on recommendation or personalization systems recently. This is something that hasn't been done in any other large company's recommender system as far as I'm aware. It seems like a paradigm shift to me, albeit, is only possible because of the addictive nature of the app. So at least for similar UIs, this concept makes a lot of recsys research irrelevant and is highly novel. As such, I don't think you're giving OP or TikTok enough credit.