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.

翻译：在本文中,我们研究如何有效利用Dense Retrievers(DRs)中的隐含反馈。我们考虑了从历史点击日志中点击数据的具体案例,作为隐含反馈。然后我们利用这些历史隐含互动来提高DR的有效性。我们研究的一个关键挑战是点击信号中的偏见,例如立场偏见,对DRs的影响。为了克服与存在这种偏见有关的问题,我们提议采用反事实Rocchio(CoRocchio)算法,利用Dense Retrievers中的隐含反馈。我们从理论和经验上表明,与Corocchio学会的密集查询代表对定位偏见是不带偏见的,并导致更高的检索效率。我们介绍了拟议方法和实验框架的执行情况,以及在https://github.com/ielab/Counterfactual-DR中取得的所有结果。