  Preference elicitation explicitly asks users what kind of recommendations
they would like to receive. It is a popular technique for conversational
recommender systems to deal with cold-starts. Previous work has studied
selection bias in implicit feedback, e.g., clicks, and in some forms of
explicit feedback, i.e., ratings on items. Despite the fact that the extreme
sparsity of preference elicitation interactions make them severely more prone
to selection bias than natural interactions, the effect of selection bias in
preference elicitation on the resulting recommendations has not been studied
yet. To address this gap, we take a first look at the effects of selection bias
in preference elicitation and how they may be further investigated in the
future. We find that a big hurdle is the current lack of any publicly available
dataset that has preference elicitation interactions. As a solution, we propose
a simulation of a topic-based preference elicitation process. The results from
our simulation-based experiments indicate (i) that ignoring the effect of
selection bias early in preference elicitation can lead to an exacerbation of
overrepresentation in subsequent item recommendations, and (ii) that debiasing
methods can alleviate this effect, which leads to significant improvements in
subsequent item recommendation performance. Our aim is for the proposed
simulator and initial results to provide a starting point and motivation for
future research into this important but overlooked problem setting.
