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I think what doesn't work is their assumption that amount of time talking means it was a "good match." It's quite possible that it took some time for someone t
by robbrown451 5y ago
I think what doesn't work is their assumption that amount of time talking means it was a "good match." It's quite possible that it took some time for someone to figure out the other person was awful, or even that the entire conversation was negative, such as an argument. The algorithm doesn't know that because it is purely based on talk time, rather than taking into account such things as if the users gave each other good ratings.
- pyinstallwoes 5y agoWouldn't that trend towards optimal over time? Perhaps with a decay or bias for recency. Valuable connection = what you put time into, most latest
- bagels 5y agoThe hypothesis they chose is not the most obvious one to attack, and it ignores the most obvious application of collaborative filtering. Their hypothesis is that if A likes B, and A likes C then C will like B. They should instead attack this hypothesis (much more common application of collaborative filtering, think predicting what book you might buy on Amazon): A likes B, A likes C, D likes B -> D has higher probability of liking C as well
- robbrown451 5y agoBut to them, A liking B and B liking A are one and the same, since all they are measuring is talk time. Regardless, I think it would do much better if it took into account ratings. Even better if they allow you to rate them on several things and then give an overall rating. I personally hate it when things try to learn from my behavior rather than my explicit declarations. For instance, if I click on clickbait (hey, I'm human) it shouldn't assume I actually like that thing or want to see more of it.