Relevance Feedback with Brain Signals
CoRR(2023)
摘要
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.
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