Proposition of the Context-Aware Application Prediction Mechanism for Mobile Devices
Proposition of the Context-Aware Application Prediction Mechanism for Mobile Devices
复制标题
移动设备上下文感知应用预测机制的提出
DOI：
10.1109/wi-iat.2013.163
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
and Masayuki Numao
中科院分区：
文献类型：
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作者：
Satoshi Kurihara;Koichi Moriyama;and Masayuki Numao
近年来，诸如智能电话和汽车导航系统的高功能移动的设备被广泛使用。这些对我们的日常生活很重要，因为我们随时随地使用它们的应用程序。然而，随着这些设备上可用的各种应用，选择合适的应用变得更加困难。因此，我们需要一种机制，为我们推荐合适的应用程序，这应该取决于用户的上下文，因为他/她在每个上下文中使用他/她的设备不同。本文指出用户在日常生活中执行的应用程序遵循幂律规律，并提出了一种在移动的设备中发现上下文感知应用程序的新方法。该方法是基于词频-逆文档频率（TF-IDF），这是用于提取重要的关键字在文档中。此外，我们还提出了一种使用这种方法的应用程序推荐机制。实验结果表明，该推荐机制比基于朴素贝叶斯的推荐机制更有效。
In recent years, highly-functional mobile devices such as smart phones and car navigation systems are widely used. These are important for our daily life because we use their applications anywhere and anytime. With the variety of applications available on these devices, however, it becomes more difficult to choose an appropriate application. Therefore we need a mechanism that recommends us suitable applications, which should depend on a user's context because he/she uses his/her devices differently in every context. This paper shows that it follows a power law what applications a user executes in daily life, and proposes a novel approach to find context-aware applications in the mobile devices. This approach is based on the term frequency - inverse document frequency (TF-IDF), which is used for extracting important keywords in a document. Moreover, we propose an application recommendation mechanism using this approach. Experimental results show that this recommendation mechanism is more effective than the mechanism using Naive Bayes.