论文标题
可解释的信息检索：调查
Explainable Information Retrieval: A Survey
论文作者
论文摘要
可解释的信息检索是一个新兴研究领域，旨在建立透明且值得信赖的信息检索系统。鉴于在搜索系统中使用复杂的机器学习模型的越来越多，解释性对于构建和审核负责任的信息检索模型至关重要。这项调查填补了可解释信息检索的原本局部多样性文献中的重要空白。它分类并讨论了针对信息检索中不同应用领域开发的最新解释性方法，提供了一个共同的框架和统一的观点。此外，它反映了评估解释并突出开放挑战和机遇的普遍关注点。
Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine learning models in search systems, explainability is essential in building and auditing responsible information retrieval models. This survey fills a vital gap in the otherwise topically diverse literature of explainable information retrieval. It categorizes and discusses recent explainability methods developed for different application domains in information retrieval, providing a common framework and unifying perspectives. In addition, it reflects on the common concern of evaluating explanations and highlights open challenges and opportunities.
