This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Sometimes you can be too good at your job. Take streaming services, like Spotify or Netflix.
With engagement as their main goal, their recommendation algorithms typically identify the content a user engaged with most in the recent past and efficiently suggest more of the same. Content and genres that received limited engagement over a few weeks or months typically get dropped. But as marketing professor Samsun Knight points out in new research, familiarity isn't the same as taste, which can take longer to develop.
Hip-hop took a couple of decades before it became a top seller, well beyond the timeframe streaming services allow for assessing what users like. The study, "Engagement-based curation and the evolution of taste," appeared in the Journal of Cultural Economics. It's also possible to have too much of a good thing, as anyone who's ever played the life out of a beloved song or gone to every action movie sequel knows.
What once brought joy can become the last thing you want to hear, watch or read. "The same optimization that maximizes engagement this quarter is the one that breeds staleness and churn—and eventual broader disengagement," says Knight, an assistant professor of marketing at the University of Toronto's Rotman School of Management. "There's a real risk of being very good at a short-term metric while slowly degrading the long-term product." Knight isolates how that happens using a model of an engagement-based curation system, drawing on a longstanding economic theory that says a taste for something increases with exposure.
His model shows consumer engagement grows with moderate exposure, peaks, then declines as exposure grinds on. A too-short exposure horizon is one reason. But another is that the algorithms don't recognize that users' past engagement has more to do with what the algorithm sent before than with users' independently acquired tastes.
"If the platform treats people's current tastes as a fixed fact about them, rather than something it helped create, it ends up confirming its own past choices," says Knight. "It keeps serving the familiar, that familiar content keeps engaging well, and the data seems to vindicate the strategy." Another downside is that new artistic movements and experiments may die before they have a chance to be born, and users may be denied the chance to get to know and love something very different from what they've been accustomed to. Imagine a world without hip-hop or electronic dance music.
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