| These are great ideas. I'll need to ponder some of them a bit, although a few immediate thoughts are below. If you have any thoughts in the future, my email is chas@idina.com (and _b is ben@idina.com). Like _b mentioned below, stories are ranked by ML fitting a bunch of signals and the article text to a user's ratings. Usage metrics is something we want to figure out how to use well. Currently we track them, but don't use them. Confession regarding images/colors/contrasts/etc: neither of us are UI people. It is something we struggle with, so doubly appreciate the feedback here. Will try to improve this soon. Regarding some missing links to reddit, are there good threads on reddit we should be linking to for them, or has reddit just not picked them up? Our algorithms try to find every related reddit thread to a story, although they can make mistakes. Regarding autofollowing all defaults -- it's tricky. We used to do this, and got feedback that people didn't link having to unfollow a bunch of topics when they created an account. Exactly how to package the experience is an open question, one that is complicated by the desire to have a good logged-out experience that transitions as smoothly to the logged-in experience as possible. Point being, you hit on a good issue here that we don't yet have a perfect answer to. 'Since last visit' time range sounds neat. Will look into adding. Re: star ratings -- manually ordering articles (this should rank above that) or having up/down arrows for each article are also natural ways to provide ranking feedback. A big concern of ours here is the ability for a user to have a clean mental model of their own feedback over a long period of time. For example, feedback in the form of up/down arrows comes necessarily in the context of a specific set of articles shown at a specific time. If someone wants to review their feedback in aggregate, it might be difficult to represent it all in one place in a way that's easy to understand. On the other hand, your criticism of it is also correct. Adding additional ways to make training data, or changing how we do this, is something we are really interested in getting right. |