What is a potential consequence of personalized recommendation algorithms on user exposure?

• A. They guarantee exposure to diverse viewpoints.
• B. They tailor content based on behavior, potentially narrowing exposure and reinforcing existing preferences.
• C. They remove any personalization.
• D. They reduce engagement.

Answer: (B)

Personalized recommendation algorithms tailor what you see based on your past behavior, clicks, and engagement signals. Because they optimize for relevance and continued interaction, they tend to surface content similar to what you’ve already engaged with, which can narrow your exposure and reinforce your existing preferences. Over time this can create a filter bubble or echo chamber where diverse viewpoints and new topics are less likely to appear. The idea isn’t that they remove personalization or that exposure to diverse viewpoints is guaranteed; rather, they actively shape what you see in ways that can limit breadth and novelty while often boosting engagement by aligning with your interests.