4 ms·
I just always assumed it would be better to match up peoples likes/dislikes against one another. For example, I might really dig Brittany Spears, but I also lik
by mulletbum 9y ago
I just always assumed it would be better to match up peoples likes/dislikes against one another. For example, I might really dig Brittany Spears, but I also like Pantera. Considering how those types of music have nothing to do with each other, it would be much better to match me up against other people who have similar taste in music as me.
So let's say for example there are 1,000 people who like Spears and Pantera (specific songs). Now take those 1,000 people and compare the other artists and songs I am listening to. Let's say, of the 1,000 people who like Spears and Pantera, 680 listen to a specific song by Creed. That's a high hit rate, which means that song should be offered to me as I will most likely enjoy it. It doesn't matter that Creed doesn't sound anything like Pantera or Spears.
- deleted 9y ago[deleted]
- mediocrejoker 9y agoI am not a data scientist, but this seems like a very obvious solution to me as well so I wonder if there is a reason it's not done this way.
- hbosch 9y agoYou end up in a echo chamber and quickly exhaust your recommendations.
- ghaff 9y agoOne problem you run into is that popular artists/titles don't really give a lot of insight into what else you'll like. This applies to a lot of recommendation areas. You like Star Wars. Great. You and a billion other people. And, by the way, I can also recommend Star Wars to you just by looking at the box office returns.