摘要：We browse through hundreds of Deep web pages everyday to find information of interest. We feel happy when Deep web browsing operations provide us with necessary information; otherwise, we feel bitter. Now, the measurement of this user satisfaction has become a hot research topic. In this paper, we propose a click-through-data-based and unsupervised user satisfaction evaluation system, CNEITE, to evaluate the user satisfaction of Deep Web query result pages. It applies query type classifying, navigational query evaluating, informational/transactional query evaluating to solve the challenging tasks. We evaluated our CNEITE system on the AOL data sets, experimental results show that CNEITE achieves higher classify precision than a widely used classify method , Dtree, and higher annotate answer accuracy than method proposed in [17].
关键词：Automatic user satisfaction evaluation; Deep Web; click-through data ; analysis
