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Context-based page unit recommendation for web-based sensemaking tasks

机译:基于上下文的页面单元推荐,用于基于Web的感官任务

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Sensemaking tasks require that users gather and comprehend information from many sources to answer complex questions. Such tasks are common and include, for example, researching vacation destinations or performing market analysis. In this paper, we present an algorithm and interface which provides context-based page unit recommendation to assist in connection discovery during sensemaking tasks. We exploit the natural note-taking activity common to sensemaking behavior as the basis for a task-specific context model. Our algorithm then dynamically analyzes each web page visited by a user to determine which page units are most relevant to the user's task. We present the details of our recommendation algorithm, describe the user interface, and present the results of a user study which show the effectiveness of our approach.
机译:有意义的任务要求用户从许多来源收集和理解信息,以回答复杂的问题。这样的任务是常见的,并且包括例如研究休假目的地或进行市场分析。在本文中,我们提出了一种算法和接口,该算法和接口提供了基于上下文的页面单元推荐,以帮助在感官任务期间发现连接。我们利用感官行为常见的自然记笔记活动作为特定于任务的上下文模型的基础。然后,我们的算法会动态分析用户访问的每个网页,以确定哪些页面单位与用户的任务最相关。我们介绍了我们的推荐算法的细节,描述了用户界面,并给出了用户研究的结果,这些结果表明了我们方法的有效性。

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