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Not directly. It starts out doing random shuffling over your existing collection (local mp3s, spotify playlists and spotify saved tracks), and then uses your sk
by jacobobryant 7y ago
Not directly. It starts out doing random shuffling over your existing collection (local mp3s, spotify playlists and spotify saved tracks), and then uses your skipping data to build the model.
If you've got a spotify premium account, it'll use spotify's recommendation API to occasionally mix in new songs based on your current listening session. Presumably their API uses objective metrics.
- p1esk 7y agoDo you see the value of incorporating objective similarity metrics into your recommendation engine? I don't know if you can tap into something like Gracenote API, but I'm sure it would improve the quality of your recommendations.
- jacobobryant 7y agoYeah, I'm planning to integrate as many metrics as I can. The key thing is just that the system has to be able to adapt on the fly. So Lagukan will have all these different inputs/different recommendation engines, but it'll only use them as hypotheses to be tested rather than the final word. As we get more data about an individual user, the system will figure out how effective the different engines are for that particular user. So it's on the roadmap for sure. I'm hoping to get more scaffolding up so I can have some kind of revenue stream, and then I'll start to iterate much more heavily on the algorithm.