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One of the reasons, I suspect, is that there is a lack of feedback. If you recommend something to a friend, later on you can ask them about it and they'll tell

by lottin 8y ago

One of the reasons, I suspect, is that there is a lack of feedback. If you recommend something to a friend, later on you can ask them about it and they'll tell you whether they liked it or not and why. Whereas these recommendation engines keep recommending things but have no way of knowing whether the recommendations they make are any good.

mooreds 8y ago
Wow, this is very perceptive and could possibly be mitigated. For instance, a lot of the recommendation engines I've looked at use basic actions (like clicks) to determine whether something is worth surfacing. But if you could to not just clicks but something more material (like time on a page, a purchase or social share) into the mix, I think recommendations would get better. Tying into feedback further removed from the initial action, if it could scale, would help the model as well.
- chudi 8y ago
  The guys from yt explains this in a paper that talks about their recsys. What they actually do is once you have a candidate set of videos, pick the best ones that maximizes the total time of watch time so they can show you more ads. That's the reason the videos that are frequently recommended are the really long duration ones
  - mooreds 8y ago
    Ah. Of course what you optimize depends on what you consider valuable, so that makes total sense. But if you want to optimize for customer satisfaction, maybe the video watched to completion or the one shared is better than the one the total amount of watch time.
DonaldFisk 8y ago
This can be done by leave-one-out cross-validation in a collaborative filtering system. If jane saw schindler's list and gave it 8/10, delete this from the database and use the rest of the data to predict it, then compare the predicted score with the score she gave. I did this for a movie recommendation system i developed: http://www.fmjlang.co.uk/morse/MORSE-Paper.html http://www.fmjlang.co.uk/morse/MORSE-Paper.html

by lottin 8y ago

mooreds 8y ago

Wow, this is very perceptive and could possibly be mitigated. For instance, a lot of the recommendation engines I've looked at use basic actions (like clicks) to determine whether something is worth surfacing. But if you could to not just clicks but something more material (like time on a page, a purchase or social share) into the mix, I think recommendations would get better. Tying into feedback further removed from the initial action, if it could scale, would help the model as well.

chudi 8y ago
The guys from yt explains this in a paper that talks about their recsys. What they actually do is once you have a candidate set of videos, pick the best ones that maximizes the total time of watch time so they can show you more ads. That's the reason the videos that are frequently recommended are the really long duration ones
- mooreds 8y ago
  Ah. Of course what you optimize depends on what you consider valuable, so that makes total sense. But if you want to optimize for customer satisfaction, maybe the video watched to completion or the one shared is better than the one the total amount of watch time.

DonaldFisk 8y ago

This can be done by leave-one-out cross-validation in a collaborative filtering system. If jane saw schindler's list and gave it 8/10, delete this from the database and use the rest of the data to predict it, then compare the predicted score with the score she gave. I did this for a movie recommendation system i developed: http://www.fmjlang.co.uk/morse/MORSE-Paper.html http://www.fmjlang.co.uk/morse/MORSE-Paper.html