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Context-aware adaptive recommendation of resources for mobile users in a university campus

机译:大学校园中移动用户资源的上下文感知自适应推荐

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The selection of resources within a university campus is not an easy task, due the multitude of physical and virtual resources available. By this fact, a person can easily select a set of resources that cannot attend their needs and interests in the best way. Such multitude of resources can leads to the problem of the person cognitive overload, which happens during the resources selection. This paper presents a novel recommender approach as an alternative to select such best set of resources and avoid the person cognitive overload during it. Due to specificities for recommending learning resources, a knowledge-based approach seems to be more effective to this task and take the context in consideration can help in selecting the best resources to the current user situation. Take in consideration an adaptive interface makes the recommendations non-intrusive and help the user to see the location of the resources in the environment. To validate our approach we are developing a system to be used in an application scenario presented in this paper.
机译:由于存在大量可用的物理和虚拟资源,在大学校园内选择资源并非易事。因此,一个人可以轻松地选择一组无法以最佳方式满足其需求和兴趣的资源。如此大量的资源会导致人的认知超负荷的问题,这会在资源选择过程中发生。本文提出了一种新颖的推荐方法,作为选择此类最佳资源集并避免人员在此过程中认知超负荷的替代方法。由于推荐学习资源的特殊性,基于知识的方法似乎对于此任务更为有效,并且考虑到上下文可以帮助选择适合当前用户情况的最佳资源。考虑到自适应界面,使建议不具干扰性,并帮助用户查看环境中资源的位置。为了验证我们的方法,我们正在开发一个可在本文介绍的应用场景中使用的系统。

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