Lex thank you for a great interview with Cristos Goodrow!<br><br>One thing that wasn’t clear to me from the discussion is whether the YouTube learning algorithm is driven by the success metrics that Cristos discussed, or presupposes a structure.  <br><br>For example in all of the discussion about collaborative filtering I could envision the system learning how videos are grouped into clusters by user behavior, how various users are similar to each other (another kind of clustering), and then learning how similar sets of users move from cluster to cluster as their interest evolves.   None of this requires any use of satisfaction metrics.<br><br>But on the other hand I could instead imagine a system that uses the success metrics such as likes/dislikes, after-the-fact surveys, etc as the primary training metric and will try to move users towards things that they are most likely to rate highly with those metrics instead of following what similar users would do.  In this scenario, the clustering and collaborative filtering would become more of a data compression technique to make things more manageable, rather than the thing being modeled as a goal per se.<br><br>In other words, is the learning algorithm trying to most perfectly match what a similar user would “do” or most perfectly match what a similar user would “like”?<br><br>In the A/B testing discussion towards the end, it seemed to lean heavily towards the “like” answer but that discussion seemed to be relating to after the fact fine tuning (more of a traditional programming task) rather than metrics which were part of the learning algorithms themselves.