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Two obvious points: why not machine learning, and a more explicit mix of exploration and exploitation Meaning give more posts a chance, a certain number of vie
by bluelightning2k 4y ago
Two obvious points: why not machine learning, and a more explicit mix of exploration and exploitation
Meaning give more posts a chance, a certain number of views to determine the upvote rate.
Conventional wisdom would apply spam filter, boost new users (e.g. first post), detect blatant manipulation (or more sophisticated manipulation using clustering).
- muyuu 4y agoWith machine learning you have the same kind of problems with an added factor of unpredictability. ML to make the recommendations more dynamic per user means people are not seeing the same things and engagement tanks. ML to make the recommendations more global means a lot of people are going to see stuff they don't care about and they're going to have no idea why. It's usually this family of problems that ML adds an extra layer of opacity to the most. Then you add extra tabs to give the user more control, and now you've added a fresh complexity trade off. The sanest aggregators have simplistic recommendation algorithms and a bunch of janitorial work on top in the form of moderation/censorship. There must be something to this formula.