Users

 

 

 

We all have our favorite songs; songs we love and cherish, songs that resonate with our souls, songs that we can listen to over and over and over again without losing the magical sentiment we feel when we hear them. Have you ever wondered how we come to know about these songs? Was it by being forced to listen to your parents’ favorite songs? Or by listening to the radio while carpooling with your school friends after an afternoon practice? Or could it have been by being in the right spot, at the right time while getting your morning coffee at the local Starbucks? Wherever or however you may have discovered your most-listened-to songs, I can bet that it probably wasn’t on your Spotify Explore Page. I am sure I am not the only one who has been disappointed and unimpressed by the results of my ‘specially-curated’ playlists, feeling stuck in a cycle of listening to the same songs over and over again or having mediocre recommendations that didn’t at all interest me. Camila Cabello is an example of an artist that makes a regular appearance on my Explore page even though I have not interacted with any of her content. 

As depicted below, we have not ‘liked’ any of Camila Cabello’s songs or albums.

Despite this fact, Cabello herself makes an appearance not once, but twice in my weekly recommendations.

 

To further exemplify this predicament, I ‘liked’ the song “This City Remix” by Sam Fischer and Anne-Marie. I head over to my Discover Weekly to find some songs that might be similar and as pleasing only to find “This City Remix” by Sam Fischer and featuring Kane Brown. 

What I want is something new and fresh, not something that I already like repackaged and delivered to me. To further understand the reasoning behind this, we need to investigate the recommendation system. So, in the next section, we will introduce the audience to the intricacies and tendencies of recommender systems.

We want to highlight the flaws of the current recommendation system through an in-depth analysis of recommendation system technology, and enhance our argument that not all artists are equally promoted further with a professional perspective on what to realistically expect from artificial intelligence and why that matters for music users. Click on the tabs on the user drop-down menu to learn more. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

We want to highlight some of the flaws of the current recommendation system through an in-depth analysis of song categorization, and technology organic to recommendation systems, and enhance our argument further with a professional perspective on the optimal results to be expected by artificial intelligence.