Algorithmic recommender systems attempt to predict which items a target
user will like based on information about the user's prior preferences
and the preferences of a larger community. After more than a decade of
widespread use, researchers and system users still debate whether such
"impersonal" recommender systems actually perform as well as human
recommenders. We compare the performance of MovieLens algorithmic
predictions with the recommendations made, based on the same user
profiles, by active MovieLens users. We found that algorithmic
collaborative filtering outperformed humans on average, though some
individuals outperformed the system substantially and humans on average
outperformed the system on certain prediction tasks.