27 ms·

Yeah, I was thinking of the same thing. It's not my field professionally though, so I am not sure what the relevant conclusion would be for machine learning tho

• BillinghamJ 11y ago
  I don't think that's quite correct. Recommendation engines normally work by taking the things you favor, then looking at who else favors those things, and deducting that you're all likely to have shared interests.
  • masklinn 11y ago
    Yeah I'd assume the simplest recommendation engines work on correlation. A recommendation engine working on average would be complete garbage, you'd be better off picking recommendation at random.
    • yeukhon 11y ago
      Quote emcq's response above: > If you're thinking about the cold start problem when you dont have any information about a user, yes it's possible that your overall statistics is a combination of many subpopulations that doesn't really fit anyone very accurately but there are ways around this as well. I'd say a better approach (but may have user experience penalty) would don't even decide for the user, ASK. For example, take Flipboard / Quora as an example, you are asked to choose some topics to follow at the beginning. Assume research/data show 80% of the users are software engineers and 90% of them always pick "technology" as a topic they want to follow, would you rather show technology as one of the top five in the list of topics to choose? There's actually a lot of experiments you can do from a simple selection/survey process. I personally can't stand at going through pages to find something relevant, but I am also surprised to find things I never thought would be interesting to follow if I weren't present the options at all / or earlier.