The tasks of legal case retrieval have received growing attention from the IR community in the last decade. Relevance feedback techniques with implicit user feedback (e.g., clicks) have been demonstrated to be effective in traditional search tasks (e.g., Web search). In legal case retrieval, however, collecting relevance feedback faces a couple of challenges that are difficult to resolve under existing feedback paradigms. First, legal case retrieval is a complex task as users often need to understand the relationship between legal cases in detail to correctly judge their relevance. Traditional feedback signal such as clicks is too coarse to use as they do not reflect any fine-grained relevance information. Second, legal case documents are usually long, users often need even tens of minutes to read and understand them. Simple behavior signal such as clicks and eye-tracking fixations can hardly be useful when users almost click and examine every part of the document. In this paper, we explore the possibility of solving the feedback problem in legal case retrieval with brain signal. Recent advances in brain signal processing have shown that human emotional can be collected in fine grains through Brain-Machine Interfaces (BMI) without interrupting the users in their tasks. Therefore, we propose a framework for legal case retrieval that uses EEG signal to optimize retrieval results. We collected and create a legal case retrieval dataset with users EEG signal and propose several methods to extract effective EEG features for relevance feedback. Our proposed features achieve a 71% accuracy for feedback prediction with an SVM-RFE model, and our proposed ranking method that takes into account the diverse needs of users can significantly improve user satisfaction for legal case retrieval. Experiment results show that re-ranked result list make user more satisfied.

翻译：法律案例检索任务在过去十年中日益受到信息检索领域的关注。基于隐式用户反馈（如点击行为）的相关性反馈技术已在传统搜索任务（如网络搜索）中显示出有效性。然而，在法律案例检索中，收集相关性反馈面临现有反馈范式难以解决的两个挑战。首先，法律案例检索是一项复杂任务，用户通常需要详细了解案例之间的法律关系才能准确判断其相关性。传统反馈信号（如点击）过于粗略，无法反映细粒度的相关性信息。其次，法律案例文档通常篇幅较长，用户往往需要数十分钟来阅读和理解。当用户几乎点击并查阅文档的每个部分时，简单的行为信号（如点击和眼动注视）很难发挥作用。本文探索利用脑电信号解决法律案例检索中反馈问题的可能性。脑电信号处理的最新进展表明，通过脑机接口可以在不中断用户任务的情况下实现对人情绪状态的细粒度采集。因此，我们提出一个利用脑电信号优化检索结果的法律案例检索框架。我们收集并创建了包含用户脑电信号的法律案例检索数据集，并提出多种提取有效脑电特征用于相关性反馈的方法。所提特征在基于SVM-RFE模型的反馈预测中达到71%的准确率，同时我们提出的考虑用户多样化需求的排序方法能显著提高法律案例检索的用户满意度。实验结果表明，重排序后的结果列表能使用户更加满意。