Data Driven Usability: A Case for Adaptive Interfaces in Voice Based Menu Systems

Siddhartha Asthana, Pushpendra Singh
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Abstract

Interactive Voice Response (IVR) systems provide access to information over the phone by responding to a pre-defined menu either through key presses or voice commands. Despite being in use for a long period, IVR systems are still considered time-consuming and frustrating to use. In this work, we show that a significant portion of the call duration goes in selecting the correct menu option. Since the menu options are presented sequentially, more time is required to access menu options appearing later in the sequence, therefore, to reduce this, relevant menu options must appear early in the sequence. In this paper, we present our data driven prediction algorithms for adaptive rearrangement of menu options so that the relevant options appear early. We also show that adaptive approaches to decide the menu structure outperform existing static menu based IVR system. We have designed, deployed, and evaluated our schemes in the real world study.
数据驱动的可用性:语音菜单系统中自适应界面的案例
交互式语音应答(IVR)系统通过按键或语音命令响应预先定义的菜单,从而在电话上提供对信息的访问。尽管使用了很长一段时间,IVR系统仍然被认为是耗时和令人沮丧的使用。在这项工作中,我们表明呼叫持续时间的很大一部分是在选择正确的菜单选项上。由于菜单选项是按顺序呈现的,因此需要更多的时间来访问序列中较晚出现的菜单选项,因此,为了减少这种情况,相关的菜单选项必须在序列中较早出现。在本文中,我们提出了一种数据驱动的预测算法,用于菜单选项的自适应重排,使相关选项尽早出现。我们还表明,自适应方法来决定菜单结构优于现有的基于静态菜单的IVR系统。我们已经在现实世界的研究中设计、部署和评估了我们的方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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