EXPRESS: Preference Filtering: When Customers Share Narrow Preferences with Algorithms

Digital platforms commonly ask customers to select interest categories (e.g., genres/topics) as input for personalized recommendations. Twelve main studies and two pilot studies (total N = 8,824) reveal that customers share less diverse preferences with algorithms (versus human curators or when merely listing preferences for themselves); they focus on core preferences while omitting tangential ones, a phenomenon termed preference filtering . It is driven by customers’ expectation that algorithms weigh their preferences more uniformly than human curators (i.e., expected uniformity). A mathematical model, as well as interviews and a survey with practitioners show that preference filtering appears rational ex ante yet leads to negative consequences for both customers and firms ex post. The authors examine key design dimensions of the preference elicitation task— when preferences are elicited, how customers articulate them, what purpose is made salient, and who customers believe they are interacting with—that determine the extent to which customers engage in preference filtering. Two studies on self-developed video-streaming websites show that alleviating preference filtering can boost engagement and enhance customer reviews of recommendation services. These findings offer valuable insights for firms that rely on algorithms to engage customers.