Do you remember how you would have to buy an entire CD just to hear one song, or spend money on a big cable package just to watch one channel? Spotify and Netflix promised to fix that. With streaming, you get to listen to anything or watch whatever you want at any time you want. This is, at least in theory, the Long Tail effect , websites can make money from niche content as much as from blockbusters and “decoupling,” where you’re billed for what you consume and not a thing extra. But does it really work this way?
Evidence shows not quite. Klimashevskaia et al. (2024) show that algorithms are built on a high popularity bias. Instead of offering everyone greater diversity, they prefer to recommend what is popular. A 2025 study goes one further, showing that platforms not only mirror popularity, they reinforce it, propelling hits even higher (Kowald, 2025).
On the user end, Netflix viewers often feel they’ve got too much choice and still watch the same suggestions over and over (Romero Meza & D’Urso, 2024). On Spotify, playlists dominate. Pachali et al. (2025) found that ending up on the right playlist can make or break a song’s visibility. So while the library is infinite, most attention stays fixed on the same few artists or shows.
I think this raises a big question about fairness. Carnovalini, et al. (2025) argue that fixing popularity bias means balancing efficiency with diversity. Personally, I’d love if Spotify or Netflix highlighted more “hidden gems,” not just the Top 10. It would make the experience more exciting for users and more rewarding for creators.
And what about you? Do you tend to go and look up niche material, or do you stick with the advised? Should Netflix and Spotify do more to promote diversity, even if it doesn’t feel as efficient?
Reference
Anastasiia Klimashevskaia, et al. “A Survey on Popularity Bias in Recommender Systems.” User Modeling and User-Adapted Interaction, vol. 34, 1 July 2024, https://doi.org/10.1007/s11257-024-09406-0.
Kowald, D. (2025). Investigating popularity bias amplification in recommender systems employed in the entertainment domain. In Proceedings of the Fourth European Workshop on Algorithmic Fairness (EWAF’25) (pp. 1–7). Proceedings of Machine Learning Research. https://doi.org/10.48550/arXiv.2504.04752
Filippo Carnovalini, et al. “Popularity Bias in Recommender Systems: The Search for Fairness in the Long Tail.” Information, vol. 16, no. 2, 19 Feb. 2025, pp. 151–151, www.mdpi.com/2078-2489/16/2/151, https://doi.org/10.3390/info16020151.
Meza, Laura Romero, and Giulio D’Urso. “User’s Dilemma: A Qualitative Study on the Influence of Netflix Recommender Systems on Choice Overload.” Psychological Studies, vol. 69, no. 3, 24 Sept. 2024, https://doi.org/10.1007/s12646-024-00807-0.
Pachali, Max J, and Hannes Datta. “What Drives Demand for Playlists on Spotify?” Marketing Science, vol. 44, no. 1, 18 Sept. 2024, https://doi.org/10.1287/mksc.2022.0273.
I still remember when a song cost €0.99 on iTunes and you had to go to blockbuster and decide on one move for the weekend
An interesting conflict of interest here, is when platforms are paid to showcase certain videos/series/songs. I know Spotify does this. Songs that will be recommended to you are decided by the songs you normally listen to, but also by who pays Spotify the most money. This means that while the number of songs is virtually unlimited, the algorithm will push those with the most money, even if that is not your usual music taste. And the only ones able to pay for such promotion are the artists who are already big, leading to even more popularity bias! Personally, I hate this feature, but I also understand that Spotify needs its revenue, an interesting discussion, I think.
On Spotify, it’s always the same: I’ll search for something niche, but the app quickly pushes me back toward the same old “safe”. Even when there’s an endless library, it feels like discovery is gated by what the algorithm decides is “safe” or popular.
I feel like whenever I listen to a song on Spotify, I tend to stick around the same artist and his songs over and over. So, surely the algorithm keeps recommending songs from that specific artist or the same kind of music style. In most of the time, these are the popular artists, so they keep getting bigger, which automatically leads to more popularity bias. Personally, I love the fact that Spotify keeps recommending the same music vibes, so I don’t have to search by myself. I can just put up a playlist and keep on listening to music I like 🙂
I completely agree. Every time I found music from small artists that I genuinely liked, I tried finding similar music and always got recommendations from big artists that I already knew. In theory, it’s a great concept, but I think it strongly depends on how you use it. I like that you also questioned the example of the long tail for this specific case; I hadn’t really viewed it from this perspective before!