[rcheu]

There’s not much need for turnover, plenty of companies publish quite a bit on how their systems work. You can look for recsys papers from your favorite company if you want. The Netflix recommendation systems workshop is also good and has many industry talks.

The reality is that there’s a lot of (what is essentially) matrix factorization, and not much of anything nefarious or very interesting to most people who are not recommendation system engineers.

Another thing is that these systems are intentionally hard to game even if you know the weights. The systems optimize for things like P(comment|personalized features), so even if you know there’s a high weight in the scoring function for comments, just commenting a lot on your content isn’t sufficient. You need the system to predict that other people will also comment.