利用大脑信号进行相关性反馈

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ziyi Ye, Xiaohui Xie, Qingyao Ai, Yiqun Liu, Zhihong Wang, Weihang Su, Min Zhang
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引用次数: 0

摘要

相关性反馈(RF)过程依赖于对反馈文档进行准确和实时的相关性估计,以提高检索性能。由于收集明确的相关性注释会给用户带来额外负担,因此大量研究都在探索使用伪相关性信号和隐式反馈信号作为替代。然而,这些信号都是相关性的间接指标,在用户互动缺失或有偏差的复杂搜索场景中会受到影响。最近,便携式高精度脑机接口(BCI)设备的发展为监测用户在搜索过程中的大脑活动提供了可能。脑信号可以直接反映用户对搜索结果的心理反应,因此可以作为额外的、无偏见的射频信号。为了探索大脑信号在搜索相关性方面的有效性,我们提出了一个新颖的搜索相关性框架,该框架将基于 BCI 的相关性反馈与伪相关性信号和隐式信号相结合,以提高文档重新排序的性能。在用户研究数据集上的实验结果表明,在我们的 RF 框架中,结合大脑信号可显著提高性能。此外,我们还观察到大脑信号在几种困难搜索场景中表现尤为出色,尤其是在作为反馈的隐含信号缺失或存在噪声的情况下。这揭示了何时以及如何在射频范围内利用大脑信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Relevance Feedback with Brain Signals

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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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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