Content Prioritization Based on Usage Pattern Analysis

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.