面向qos感知的普适学习的模糊推荐

Zhiwen Yu, Norman Lin, Yuichi Nakamura, S. Kajita, K. Mase
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引用次数: 13

摘要

普适学习承诺提供一个令人兴奋的学习环境,这样用户就可以随时随地通过任何设备访问和学习内容。除了向学习者提供正确的内容外,还需要在呈现内容方面提供可接受的服务质量(QoS)保证。本文提出了一种基于模糊逻辑理论的qos感知普适学习推荐方法。它根据用户的QoS要求和设备/网络能力,确定合适的学习内容的表示形式。我们还提出了一种自适应QoS映射策略,该策略在运行时根据客户端设备的能力动态设置质量参数。实验结果表明,该方法是可行的,可以实现qos感知的普适学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy Recommendation towards QoS-Aware Pervasive Learning
Pervasive learning promises an exciting learning environment such that users can access content and study them at anytime, anywhere, through any devices. Besides delivering the right content to the learner, it is necessary to provide acceptable Quality-of-Service (QoS) guarantees in terms of presenting the content. In this paper, we propose a recommendation approach based on fuzzy logic theory towards QoS-aware pervasive learning. It determines appropriate presentation form of the learning content according to user's QoS requirements and device/network capability. We also propose an adaptive QoS mapping strategy, which dynamically sets quality parameters at running time according to the capabilities of client devices. The experimental results show the proposed approach is feasible and acceptable to enable QoS-aware pervasive learning.
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