  Many recommendation algorithms rely on user data to generate recommendations.
However, these recommendations also affect the data obtained from future users.
This work aims to understand the effects of this dynamic interaction. We
propose a simple model where users with heterogeneous preferences arrive over
time. Based on this model, we prove that naive estimators, i.e. those which
ignore this feedback loop, are not consistent. We show that consistent
estimators are efficient in the presence of myopic agents. Our results are
validated using extensive simulations.
