The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on the user, extensive studies have explored using pseudo-relevance signals and implicit feedback signals as substitutes. However, such signals are indirect indicators of relevance and suffer from complex search scenarios where user interactions are absent or biased. Recently, the advances in portable and high-precision brain-computer interface (BCI) devices have shown the possibility to monitor user's brain activities during search process. Brain signals can directly reflect user's psychological responses to search results and thus it can act as additional and unbiased RF signals. To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based relevance feedback with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking. The experimental results on the user study dataset show that incorporating brain signals leads to significant performance improvement in our RF framework. Besides, we observe that brain signals perform particularly well in several hard search scenarios, especially when implicit signals as feedback are missing or noisy. This reveals when and how to exploit brain signals in the context of RF.

翻译：相关性反馈（RF）过程依赖于对反馈文档进行准确且实时的相关性评估，以提升检索性能。由于收集显式相关性标注会给用户带来额外负担，大量研究探索了使用伪相关性信号和隐式反馈信号作为替代。然而，这些信号仅是相关性的间接指标，在用户交互缺失或存在偏差的复杂搜索场景中效果不佳。近年来，便携式高精度脑机接口（BCI）设备的进步使得监测用户搜索过程中的脑部活动成为可能。脑信号能直接反映用户对搜索结果的心理反应，因此可作为无偏的附加RF信号。为探究脑信号在RF场景中的有效性，我们提出了一种新型RF框架，该框架融合基于BCI的相关性反馈、伪相关性信号和隐式信号，以改进文档重排序性能。用户研究数据集上的实验结果表明，引入脑信号显著提升了我们RF框架的性能。此外，我们观察到脑信号在若干困难搜索场景中表现尤为出色，特别是在隐式反馈信号缺失或包含噪声的情况下。这揭示了在RF场景中何时以及如何利用脑信号。