Brief/Problem Statement

Music recommendation engines have the following issues:

Internal Feedback Loops

Spotify recommends music based off of the music listening habits of it’s users. The system both influences the listening habits and is driven by them. This leads to a ‘long tail’ where an artist must hit a certain threshold of success to continue success, and is detrimental to more modest artists. last.fm, iTunes, and other recommendation engines that are also listening services suffer from this

Stores or users who play spotify as a radio station all day long have a disproportionate influence over the system, as their 247 number of listens is far greater than any regular listener.

People don’t know what they want to listen to

The average music listener listens to the same cycle of artists, and infrequently is discovering new bands to listen to. Not every user has listened to enough music to form a taste that is worth accounting for in data. Their listening data, in terms of building recommendation engines for new music discovery, is not as useful as a user who is actively searching for and trying out new artists.

Genres are good for talking about music, but not good for music discovery.

Attempting a sort of ‘music genome’ to algorithmically determine similar artists to recommend is hinged on the assumption that a if a user likes one artist, they will like a similar artist. This usually correct assumption leads to an unaccounted for contrapositive, where the the user will not like artists that are not similar to the original artist. This is false, and locks users into listening to only particular styles, genres, time-periods, and other taxonomic categories instead of a diverse spread of music.

New releases are rated too high.

By valuing only the ‘freshest’, ‘hottest’ album drops, recommendation engines disvalue music that is not recently produced. Look at the movie industry, where a movie must be successful in its first few weekends to make any money. This, in my opinion, is an unhealthy model for music listening, particularly as music is not ephemeral to it’s time of production, and that a user can listen to far more music than they can watch movies, and enjoy music repeatedly, coming back to music long after it’s release date. Recommendation engines that value fresh music too much encourage this environment.

One can avoid these issues, if - in the automotive spirit of big data - one embraces ‘small data’ and starts paying attention to specific users to give a service a hand-picked flair.