  Retrieving target information based on input query is of fundamental
importance in many real-world applications. In practice, it is not uncommon for
the initial search to fail, where additional feedback information is needed to
guide the searching process. In this work, we study a setting where the
feedback is provided through users clicking liked and disliked searching
results. We believe this form of feedback is of great practical interests for
its convenience and efficiency. To facilitate future work in this direction, we
construct a new benchmark termed click-feedback retrieval based on a
large-scale dataset in fashion domain. We demonstrate that incorporating
click-feedback can drastically improve the retrieval performance, which
validates the value of the proposed setting. We also introduce several methods
to utilize click-feedback during training, and show that click-feedback-guided
training can significantly enhance the retrieval quality. We hope further
exploration in this direction can bring new insights on building more efficient
and user-friendly search engines.
