  Self-reinforcing feedback loops are both cause and effect of over and/or
under-presentation of some content in interactive recommender systems. This
leads to erroneous user preference estimates, namely, overestimation of
over-presented content while violating the right to be presented of each
alternative, contrary of which we define as a fair system. We consider two
models that explicitly incorporate, or ignore the systematic and limited
exposure to alternatives. By simulations, we demonstrate that ignoring the
systematic presentations overestimates promoted options and underestimates
censored alternatives. Simply conditioning on the limited exposure is a remedy
for these biases.
