  In this paper we study how to effectively exploit implicit feedback in Dense
Retrievers (DRs). We consider the specific case in which click data from a
historic click log is available as implicit feedback. We then exploit such
historic implicit interactions to improve the effectiveness of a DR. A key
challenge that we study is the effect that biases in the click signal, such as
position bias, have on the DRs. To overcome the problems associated with the
presence of such bias, we propose the Counterfactual Rocchio (CoRocchio)
algorithm for exploiting implicit feedback in Dense Retrievers. We demonstrate
both theoretically and empirically that dense query representations learnt with
CoRocchio are unbiased with respect to position bias and lead to higher
retrieval effectiveness. We make available the implementations of the proposed
methods and the experimental framework, along with all results at
https://github.com/ielab/Counterfactual-DR.
