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.
