161–170 of 275 posts
I'm going to have to go ahead and plug SAGE[0], because between Spotify recommending me the same bands/songs ad nauseum (or just being way off), friends already telling me they used it to find a new band to listen to on the first day it launched, and just being a fan of his work in general, it's a complete labor of love and I think it shows. [0] https://medium.com/@hate5six/sage-an-artificially-intelligen...
I find the premise fascinating because of how bad the Discover Weekly recommendations were when I was looking for a replacement for Rdio. Rdio did a great job of suggesting new music but with Spotify I found myself constantly skipping tracks – combined with not having a way to play album tracks in order[1] I found no justification for using, much less paying for the service. 1. Yes, they claim to offer that for paid…
> combined with not having a way to play album tracks in order As a paid subscriber for several years, I've had no difficulties playing album tracks in order. I'm not sure what issue you were having, though.
I’m totally willing to believe that it was some config bug but the effective lack of support was quite a disappointment, especially with Rdio for comparison.
I spent quite a bit of time thinking about the recommendation algorithm and (after falling into a death metal hole I can't seem to escape on Spotify) I came to the conclusion that their analysis of the audio content of the song is way too shallow. You listen to songs because they have a common harmonic structure, or rythm, but not necessarily the same spectrum. That's why a metal fan might dig a cover of Metallica by…
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 (…
The biggest problem with the Discover Weekly is its inability to understand _why_ you're listening to a specific subset of music. It might not be that your taste in music suddenly changed, or that you discovered a new genre that you're incredibly interested in, even though your most listened to genres or songs changed for a few weeks, or your listening patterns changed for a few weeks. A few examples: I'm Norwegian,…
I think the Christmas example is a good one, I wouldn't want half my Discover to have Christmas songs- but I wouldn't mind having 1 or 2, perhaps from an artist I had never realized had a christmas single.
As an artist, Discover Weekly has been the best thing to ever happen to me. Every Monday I get a big infusion of listeners (around 5,000)— many of who stick around and check out my other music :) Prior to that, the best press I could get was the tedious process of cold-emailing bloggers (a practice which is now dying off).
Haha - read your comment, looked at your username, opened Spotify, and "Purification Ritual" is the first song in my Discover Weekly.
Earlier quoted context omitted.
Haha - read your comment, looked at your username, opened Spotify, and "Purification Ritual" is the first song in my Discover Weekly.
And now thanks to discussion of Discover Weekly, I've discovered Steve Benjamins... Thanks!
Does anyone know how they evaluate how successful their new recommendation algorithms are? How much do they improve on simpler algorithms? It's not obvious/intuitive the NLP and audio model algorithms mentioned would be that successful. I would have thought collaborative filtering + showing you new tracks from artists you like + showing you new tracks in genres you like would get you most of the way there.
I really don't understand why collaborative filtering isn't the sole approach. It's basically exactly what you want if you have that volume of data.
There might be an unknown Japanese band that'd suit my tastes perfectly and I'd never know with Collab filtering.
Does anyone know how they evaluate how successful their new recommendation algorithms are? How much do they improve on simpler algorithms? It's not obvious/intuitive the NLP and audio model algorithms mentioned would be that successful. I would have thought collaborative filtering + showing you new tracks from artists you like + showing you new tracks in genres you like would get you most of the way there.
I'm guessing they evaluate in many ways. E.g. play counts for online evaluations, accuracy compared to some ground truth for offline evaluations
Sometimes I wish Spotify added a bit more 'noise' to their recommendations, so to speak. If I don't listen to much music except Discover Weekly for a few weeks, I (subjectively) find that what's recommended to me more or less sounds the same after a while. Either they are afraid to insert new things that stray too far from an optimal recommendation or they forget too much of my listening history.
I'm a former Last.FM user. They'd play a mix of past favorites and related recommendations, then adjust based on feedback (e.g. likes). There was a noise slider that would influence the ratio of new songs (and indirectly bring in recommendations from farther away from your core tastes). When I first found out I could be more adventurous, I was very excited, as I (like most people) pride myself on being curious. As it…