The core NLP behind Prismatic is topic modeling; we don't use anything off the shelf, but something crafted pretty specifically to our needs.
We do model user similarity based upon interest overlap and social graph analysis. So we know how likely you are to care about what someone in your extended network shares.
The latter would be perfectly understandable, so not meant to be a hostile question, more curiosity out of personal interest of how you handle that aspect of private-sector research. Worries about that are part of what keeps me personally from jumping from academia. I'd like to do less paper-writing and more applied work, which fits nicely, but I'd still want to do some paper-writing and be able to discuss techniques publicly, which seems to fit more awkwardly. I assume it's possible to pull off both, but all the people I know who've left academia for startups have stopped publishing completely, and many of them doing ML/big-data stuff are quite secretive about their techniques.
In terms of industry research on the whole and publishing. It is true that you can't publish everything, but look at Google. Arguably some of the influential systems papers of the last decade or so have come out of Google. Google doesn't publish all the details of their search algorithms, but it turns out that because they address real-world problems they've done enough great stuff that some of it can safely be shared without endangering their moat.
Another aspect to consider is that while industry publishes less, we do tend to churn out useful open-sourced (Prismatic will definitely be doing that soon) that is of at least comprable utility to people out there as most papers.