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Modeling User Knowledge from Queries: Introducing a Metric for Knowledge

机译:通过查询对用户知识进行建模:引入知识度量

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摘要

The user's knowledge plays a pivotal role in the usability and experience of any information system. Based on a semantic network and query logs, this paper introduces a metric for users' knowledge on a topic. The finding that people often return to several sets of closely related, well-known, topics, leading to certain concentrated, highly activated areas in the semantic network, forms the core of this metric. Tests were performed determining the knowledgeableness of 32,866 users on in total 8 topics, using a data set of more than 6 million queries. The tests indicate the feasibility and robustness of such a user-centered indicator.
机译:用户的知识在任何信息系统的可用性和体验中都起着至关重要的作用。在语义网络和查询日志的基础上,本文介绍了一种针对用户关于主题的知识的度量。人们经常返回到几组紧密相关的,众所周知的主题,从而导致语义网络中某些集中的,高度激活的区域的发现,构成了该指标的核心。进行了测试,使用超过600万个查询的数据集,确定了总共8项主题中的32,866名用户的知识水平。测试表明了这种以用户为中心的指标的可行性和稳健性。

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