  Online marketplaces, search engines, and databases employ aggregated social
information to rank their content for users. Two ranking heuristics commonly
implemented to order the available options are the average review score and
item popularity-that is, the number of users who have experienced an item.
These rules, although easy to implement, only partly reflect actual user
preferences, as people may assign values to both average scores and popularity
and trade off between the two. How do people integrate these two pieces of
social information when making choices? We present two experiments in which we
asked participants to choose 200 times among options drawn directly from two
widely used online venues: Amazon and IMDb. The only information presented to
participants was the average score and the number of reviews, which served as a
proxy for popularity. We found that most people are willing to settle for items
with somewhat lower average scores if they are more popular. Yet, our study
uncovered substantial diversity of preferences among participants, which
indicates a sizable potential for personalizing ranking schemes that rely on
social information.
