5 ms·
Interesting point about reviewers skewing positive for popularity. My big issue with most recommendation engines is that they're biased towards false positives
by Zanni 6y ago
Interesting point about reviewers skewing positive for popularity. My big issue with most recommendation engines is that they're biased towards false positives rather than false negatives. The other way would lead to fewer, more focused recommendations.
In particular, I'd like to see an engine take a reviewer's dislikes into account more. If someone likes (switching to film for an accessible example) Cinderella, The Little Mermaid and Mulan, their opinion on Brave isn't going to carry much information as they're pretty indiscriminate about Disney films. But if they love Mulan, dislike Little Mermaid and hate Cinderella, their opinion on Brave is more likely to line up with my own.
- b0rsuk 6y agoThe "smile or die" mentality propagated by sites like FB, where you only have likes but no dislikes, is throwing half the information away. I think it stems from a misguided desire to avoid adversity.
- bbarnett 6y agoOr possibly, it stems from becoming "super reviewers", and then being paid to review works for compensation? Or the thought that such things, if not happening, may happen? Just like on Amazon and other sites?
- prepend 6y agoI would think that the challenge is to find good recommenders rather than specific recommendations. If I can find similar tastes, maybe a little bit different, then that will result in good recommendations. So I would want to find people who both like similar things but also dislike similar things. It will be interesting to find people my similarity and see which are best at finding books I like.
- Jemaclus 6y agoI follow my friends, and my friends tend to have good taste! This is essentially my strategy. I do have a few friends who have recommended books that I didn't like, so I just don't listen to their recs anymore. Solved problem! That's one of the problems I have with ML-based recommenders. I can't identify any particular ML algorithm, and therefore I can't say "This one has consistently given me bad recommendations, so I'll ignore it. But I do like this one over here." Whereas with my friends, I know that Lori and Nicole gives good recs and Brad doesn't, so I'll ignore Brad's and read recs from the other two.