The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first‐of‐its‐kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology‐based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).

中文翻译：

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在信息检索的意图建模中整合神经生理学相关反馈

使用来自神经生理学的隐式相关反馈可以提供轻松的信息检索。然而，由于噪声信号和数据的不完整或不一致表示，计算神经生理反应和检索文档都具有不确定性。我们提出了同类中第一个完全集成的信息检索系统，该系统利用通过脑电图 (EEG) 和眼球运动测量的大脑活动生成的在线隐式相关反馈。评估实验 (N = 16) 的结果表明，我们能够计算基于在线神经生理学的相关反馈，其性能明显优于复杂数据域和现实搜索任务中的偶然性。我们通过演示如何将这种固有嘈杂的隐式相关反馈与稀缺的显式反馈结合到交互式意图建模中来做出贡献。尽管任务性能的实验测量不允许我们展示分类结果如何转化为搜索任务性能，但实验证明我们的方法能够在现实场景中从大脑信号和眼动中生成相关反馈，从而为神经适应性信息检索（IR）的未来工作。