ABSTRACT

Providing appropriate help is important in smartphone development as smartphones have become increasingly complex owing to their large number of features. To determine the appropriate help content, numerous studies on contextual help systems have been conducted; however, few studies have been concerned with user manual content. Thus, to provide effective user manuals, we focused on content prioritization, considering the usage pattern. Specifically, we calculated the vector representation of each element of the usage pattern and adopted a heterogeneous embedding approach. Moreover, we embedded the entire usage pattern using RNN-SVAE to calculate a user modeling value for representing user interests. Additionally, we trained InfoGAN (a generative adversarial network) to predict the usage of the user manual, and we prioritized and re-organized its content accordingly. Experiments demonstrated that, compared with existing benchmark methods, the proposed method can achieve better content-usage prediction and more effective prioritization of the top-k contents.

摘要

提供适当的帮助在智能手机开发中很重要，因为智能手机由于其大量功能而变得越来越复杂。为了确定合适的帮助内容，已经对上下文帮助系统进行了大量研究；然而，很少有研究关注用户手册的内容。因此，为了提供有效的用户手册，我们专注于内容优先级，并考虑了使用模式。具体来说，我们计算了使用模式的每个元素的向量表示，并采用了异构嵌入方法。此外，我们使用 RNN-SVAE 嵌入了整个使用模式，以计算代表用户兴趣的用户建模值。此外，我们训练了 InfoGAN（一种生成对抗网络）来预测用户手册的使用情况，我们相应地对其内容进行了优先排序和重新组织。实验表明，与现有的基准方法相比，所提出的方法可以实现更好的内容使用预测和更有效的顶级排序。k内容。