I'm Jacob O'Bryant. I use Findka to experiment with new ways of discovering information. I started working with recommender systems during my undergrad at BYU, since I wasn't happy with Pandora's or Spotify's algorithms. I got my bachelor's degree in 2017, worked for a year at Lucid Software, then became a full-time startup founder. I started working on Findka in November 2019.
How the algorithm works
Findka uses an off-the-shelf collaborative filtering algorithm (k-NN), which compares link click data between users to find new essays you might like. I supplement this with content-based filtering, which involves analyzing the text of each essay to guess which ones are similar. I also use a technique I've dubbed "popularity smoothing," which ensures that popular essays don't get recommended too often. And to prevent filter bubbles, a portion of the articles you get are chosen more-or-less randomly (exploration vs. exploitation).
In addition, I personally review every submitted article, and I give extra weight to the ones I think are best. I'll continue this for as long as I can keep up.
that information flows
is highly inefficient
, which leads to all sorts of problems and missed opportunities. I am convinced that improving discovery is one of the most important problems of our time. If good ideas travel further, it will impact everything—from large societal challenges, like climate change and politics, to personal issues, like career choices and parenting.