Click-Feedback Retrieval
This work addresses the need for more efficient and user-friendly search engines by leveraging convenient click feedback, though it is incremental as it builds on existing retrieval methods with a new feedback mechanism.
The paper tackles the problem of improving retrieval performance when initial searches fail by incorporating user click feedback (likes and dislikes) as additional guidance, demonstrating that this approach drastically enhances retrieval quality in a fashion domain benchmark.
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.