The categories are pretty fuzzy, and I need to update the ones on the landing page. After you sign up, there's a text field where you can add arbitrary tags. And as time goes on, the algorithm will experiment with sending you different things and expand beyond whatever topics you gave it anyway. In truth, the only reason I even put those check boxes on the landing page is to help people understand what the thing even does :) (a surprisingly difficult problem for people not familiar with recommender systems).
Thanks. I'll try that.
> a surprisingly difficult problem for people not familiar with recommender systems
Do you feel like open-sourcing and/or writing a post on how it works? I feel interested in recommender systems too :-)
I'm planning to build a business on this, so probably won't open-source it--but I'm always looking for interesting things to write about! I write a weekly newsletter called Future of Discovery[1]; I might write up some more implementation details there in a week or two. In the mean time, most of the heavy lifting is done by the Surprise python lib[2]. It's pretty easy to play around with, just give it a csv of <user id>, <item id>, <rating> and then you can start making rating predictions. Also fastText[3] is easy to mess around with too. Most of the code I've written just layers things on top of that, e.g. to handle exploration-vs-exploitation as discussed in another thread here.
Recently I've been factoring out the ML code into a separate recommendation service so it can different kinds of apps (I just barely made this essay recommender system[4] start using it for example).
I'm happy to chat about recommender systems also if you like, email's in my profile.
[1] https://findka.com
[2] http://surpriselib.com/
[3] https://fasttext.cc/
[4] https://essays.findka.com