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.