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A Menu-Based Content Search System Based on Relationships between Mobile User Context and Information Needs

机译:基于菜单的移动用户上下文与信息需求之间关系的内容搜索系统

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Due to the popularization of smart phones, many people search contents in various situations (e.g., walking or sitting). It is known that user's search objective is affected by the user's situation such as time and location. Meanwhile, when users are walking, they cannot pay enough attentions to operate their devices. To assist contents search for mobile users, in our previous work, we proposed a contents search system that presents a few options that enable user to get desired contents easily according to their situations. However, in this system, we manually constructed a tree structured ontology which is used for the system to infer user's situation and search objective based on sensory data. Thus, it was difficult to handle a large number of situations and could not guarantee the accuracy of the constructed ontology. To solve these problems, in this paper, we extend our system to automatically learn the relationships between user context and information needs by using a Bayesian network. Through user experiments, we confirmed that our system could learn the relationships and infer users' situation effectively.
机译:由于智能电话的普及,许多人在各种情况下(例如,步行或坐着)搜索内容。已知用户的搜索目标受诸如时间和位置之类的用户状况影响。同时,当用户走路时,他们不能给予足够的注意力来操作他们的设备。为了帮助移动用户进行内容搜索,在我们以前的工作中,我们提出了一个内容搜索系统,该系统提供了一些选项,使用户可以根据自己的情况轻松获得所需的内容。然而,在该系统中,我们手动构建了树形本体,该本体用于系统推断用户的情况并基于感官数据搜索目标。因此,难以处理大量情况,并且不能保证所构建本体的准确性。为了解决这些问题,在本文中,我们扩展了系统以使用贝叶斯网络自动学习用户上下文和信息需求之间的关系。通过用户实验,我们确认我们的系统可以学习关系并有效推断用户情况。

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