Recommender systems use people’s opinions about items in
an information domain to help people choose other items.
These systems have succeeded in domains as diverse as
movies, news articles, Web pages, and wines. The psychological
literature on conformity suggests that in the course of
helping people make choices, these systems probably affect
users’ opinions of the items. If opinions are influenced by
recommendations, they might be less valuable for making
recommendations for other users. Further, manipulators who
seek to make the system generate artificially high or low recommendations
might benefit if their efforts influence users
to change the opinions they contribute to the recommender.
We study two aspects of recommender system interfaces that
may affect users’ opinions: the rating scale and the display
of predictions at the time users rate items. We find that users
rate fairly consistently across rating scales. Users can be
manipulated, though, tending to rate toward the prediction
the system shows, whether the prediction is accurate or not.
However, users can detect systems that manipulate predictions.
We discuss how designers of recommender systems
might react to these findings.