Collaborative exploratory search for information filtering and large‐scale information triage
提出一种协作探索式搜索策略，帮助专业信息搜寻者高效处理大规模异构数据，将海量信息压缩为高精度可读数据集，适用于金融、医疗、商业等多个行业。
Modern information seekers face dynamic streams of large‐scale heterogeneous data that are both intimidating and overwhelming. They need a strategy to filter this barrage of massive data sets, and to find all of the information responding to their information needs, despite the pressures imposed by schedules and budgets. In this applied research, we present an exploratory search strategy that allows professional information seekers to efficiently and effectively triage all of the data. We demonstrate that exploratory search is particularly useful for information filtering and large‐scale information triage, regardless of the language of the data, and regardless of the particular industry, whether finance, medical, business, government, information technology, news, or legal. Our strategy reduces a dauntingly large volume of information into a manageable, high‐precision data set, suitable for focused reading. This strategy is interdisciplinary, integrating concepts from information filtering, information triage, and exploratory search. Key aspects include advanced search software, interdisciplinary paired search, asynchronous collaborative search, attention to linguistic phenomena, and aggregated search results in the form of a search matrix or search grid. We present the positive results of a task‐oriented evaluation in a real‐world setting, discuss these results from a qualitative perspective, and share future research areas.