Spotify's rating and recommendation system leaves quite a bit to be desired. They need granularity, and they need to explain the algorithm to some extent. If I

Spotify's rating and recommendation system leaves quite a bit to be desired. They need granularity, and they need to explain the algorithm to some extent. If I like a track what exactly am I liking? The genre? The artist? Only specifically that song? Some maths that approximate a certain properties in the track? Some combination? How does liking song X influence Spotify's recommendation system on average? I have experienced the same as other posters WRT listening to a genre for a session only to have Spotify fixate on that genre until I've skewed the average in another direction. Small hack I've found if you make a playlist with a large and diverse sample size you can nudge their playlist enhancer to stay with the mood you are going for. On granularity, I would want to be able to say: Let x = track, genre, artist I don't like X, never play it. Exclude X just for this listening session. Skew toward more X I'd also imagine this would produce much richer data for future recommendation systems. If Spotify were to train on the data I've generated on their platform it would end introducing bias by the nature of me working around their systems and still not getting what I want, granted I may be an outlier. Edit: Formatting & typo