Content Prioritization Based on Usage Pattern Analysis

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.

Additional information

Funding

This work was supported by The National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [No. 2020R1F1A1067914].

Notes on contributors

Jonghwan Park

Jonghwan Park is currently pursuing a Ph.D. degree with the Department of Data Science, Seoul National University of Science and Technology, Seoul, Korea. His research interests include image processing, and data mining applications.

Younghoon Lee

Younghoon Lee is a Professor with the Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul, Korea. His research interests include user interface, machine learning, and data mining applications.