Social media, news, music, shopping, and other sites all rely on recommender systems: algorithms that personalize what each individual user sees. These systems are largely driven by predictions of what each person will click, like, share, buy, and so on, usually shorthanded as “engagement.” These reactions can contain useful information about what’s important to us, but—as the existence of clickbait proves—just because we click on it doesn’t mean it’s good.
Many critics argue that platforms should not try to maximize engagement, but instead optimize for some measure of long-term value for users. Some of the people who work for these platforms agree: Meta and other social media platforms, for example, have for some time been working on incorporating more direct feedback into recommender systems.
For the past two years, we have been collaborating with Meta employees—as well as researchers from the University of Toronto, UC Berkeley, MIT, Harvard, Stanford, and KAIST, plus representatives from nonprofits and advocacy organizations—to do research that advances these efforts. This involves an experimental change to Facebook’s feed ranking—for users who choose to participate in our study—in order to make it respond to their feedback over a period of several months.
Source: Platforms Can Optimize for Metrics Beyond Engagement | WIRED