Recommendation algorithms might be making your entertainment boring, new research suggests

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Recommendation algorithms might be making your entertainment boring, new research suggests

by<br>Eric W. Dolan

June 2, 2026

Reading Time: 6 mins read

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A recent study published in the Journal of Cultural Economics suggests that highly accurate content recommendation algorithms might accidentally make our entertainment feel boring over time. The theoretical model indicates that injecting a small amount of randomness into these systems tends to improve long-term user satisfaction. This mathematical imperfection helps people discover new tastes before they grow tired of their usual favorites.

Today, computer programs dictate the discovery of music, movies, and videos for billions of people. Platforms design these systems to maximize immediate user engagement. But researcher Samsun Knight noticed a paradox in this modern setup.

Knight is an assistant professor at the University of Toronto’s Rotman School of Management and a faculty affiliate at the University of Toronto School of Cities. He is also a novelist and a graduate of the Iowa Writers’ Workshop. His second novel, Likeness, was published in July 2025 and was named a People magazine best new book.

"I read Bourdieu’s The Rules of Art and loved it, and that helped me put a name to a number of seemingly disconnected things that I’d previously noticed about the creative algorithmic ecosystem, but hadn’t had the language to put together before," Knight said. "For example, I’d had this rather odd experience of really loving many of Spotify’s algorithmic song recommendations at first, but then was surprised to notice how Spotify belligerently kept recommending those same oh-what-a-great-find songs, until I couldn’t stand listening to them anymore."

He noticed similar patterns in his other profession. "I’m also a novelist, and had heard from a number of publishing industry professionals that the application of data-analytics tools seemed to have coincided with a massive increase in trend-chasing among publishing houses, and at the same time, many readers were complaining that a lot of big-publishing-house fiction all sounded strangely similar."

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"Given that publishing houses presumably want to be making their readers happy and Spotify wants me to keep loving the songs they recommend, I wondered why such well-resourced companies might get stuck in bad equilibria," Knight said. "This paper is the final form, so to speak, of that wondering."

A key concept in this research is what economists call consumption capital. This idea simply means that the more you consume a specific type of art, the more you build an appreciation for it. Human enjoyment of art follows an inverted curve. Moderate exposure to a style makes you like it more, but excessive exposure eventually causes boredom or satiation.

"The central idea is that because aesthetic tastes evolve slowly over multiple years, algorithms that predict ever-more-perfectly what you want to watch or listen to today may incidentally prevent us from discovering what we’d otherwise learn to love tomorrow," Knight told PsyPost. He explained that it takes a certain amount of exposure to know how to appreciate a style, while too much exposure can make a person sick of a whole type of song or show.

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"Put another way, the algorithm that can find you exactly the song you want tonight might be quietly narrowing the set of songs you’ll ever want at all," Knight said. "The most concrete example I usually mention is hip-hop."

"It took many listeners a long time to learn how to listen to hip-hop, which at first sounded abrasive and off-putting to people who were only used to listening to rock and roll," Knight said. He noted the same thing happened for rock and roll a few decades earlier. "The idea of the paper is that if Spotify was as dominant in the 1980s as it is today, listeners’ initial distaste would have pushed hip-hop far down in their algorithmic recommendation rankings, and the genre may have never gotten off the ground at all."

Recommendation algorithms usually test content over a few weeks or months. Human tastes evolve over ten or twenty years. Knight built a mathematical model to see what happens when short-sighted computer systems control all exposure to art. Because this topic involves decades of taste evolution, Knight did not recruit human participants.

Instead, the author constructed a dynamic mathematical model. A theoretical model uses mathematical equations to simulate complex human behaviors under controlled conditions. The model included two primary components. First,...