AI recommendations fail fans who like hard rock and hip hop – official science

People who listen to rock or hip hop are harder for music recommendation algorithms to please, according to a study by machine-learning experts and data scientists.

The problem appears to be this, we're told:

• People who prefer so-called hard music, which covers a wide set of genres from hard rock and punk to hip hop, aren't much interested in music outside of their niche.
• People who prefer softer music, which is a limited set of genres, are happy to listen to artists outside of their niche.
• Algorithms are better at recommending tracks for easygoing ambient fans than for choosy hard music fans, or in other words, worse at picking tracks for hard music fans than for ambient lovers.

In short, hard music fans are picky, and algorithms can't cope with these difficult-to-please folks. That might seem a trivially obvious conclusion but that's science, we guess: supporting a conclusion, even if obvious, and then finding an engineer to fix it.

“Some subgroups, like ambient listeners, seem to be more open to listening to music from other subgroups; plus, they are more similar to each other – all of this is great for recommendation algorithms and such users more likely accept recommendations from different groups,” Elizabeth Lex – co-author of this research, published in EPJ Data Science this month – told The Register.

“In contrast, hard rock and hip-hop low-mainstream listeners are, in our data, the least open to music of other subgroups, and within themselves, much more diverse, and thus, harder to satisfy with recommendations.”

The researchers, led by boffins at the Graz University of Technology (TUG) in Austria, analysed the performance of multiple recommendation engines on the music listening histories of about 4,000 people scraped from Last.fm users; the code involved is on GitHub, here.

Given some of the tracks and the type of music each person listened to, could the models correctly predict what other tunes they would enjoy? A recommendation model's accuracy was measured by seeing if the software's suggestions overlapped with tracks a particular user had actually listened to and liked. When the team took an average of how well the recommendation systems tested, they found that their predictions were most accurate for ambient listeners, and least accurate for hard music fans.

Not only are fans of hard music less likely to listen to other genres, the songs they listen to within their niche are more likely to sound distinct from one another. Meanwhile, tracks that top mainstream charts do seem to sound the same. This makes it all the more difficult for recommender systems to find relevant music for hard music fans.

While the recommendation software deployed by the likes of Spotify and Pandora are secret, the algorithms will likely be based on collaborative filtering mechanism algorithms, just like the ones used in this experiment, we're told.

“Three of these algorithms are collaborative filtering-based, that means that similar users are detected and songs of these similar users are recommended,” said Dominik Kowald, first author of the paper and a research area manager of the Social Computing team at the Know-Center, Austria.

"This is the most used type of recommender systems to date and thus, also reflects the primary ones implemented in Spotify or Pandora."

This is the most used type of recommender systems to date and thus, also reflects the primary ones implemented in Spotify or Pandora

These real-world systems will be more complex than the ones tested for this study, however, which is something to bear in mind.

“Our algorithms are more simplistic than what the streaming platforms use, particularly in terms of data they can exploit as naturally platform providers have complete access to users' data,” Lex added.

The team hopes that their study will improve music streaming services for people who prefer their particular musical niche.

“If we think about the problem from the perspective of artists, who produce low-mainstream music, if their work is recommended more often, they get more exposure and interactions – which is crucial in this business," Lex told us. "So, we hope that our research contributes to helping serve consumers better and to help low-mainstream artists get more exposure in music streaming platforms." ®

Editor's note: This article was updated after publication to include Dominik Kowald's quote, and to refine our framing of the paper.