Predicting a click sequence in mobile applications improves the user experience in various ways. By predicting which button will be clicked next, one can predict how the application will work and how the device will operate. However, predicting the click sequence is difficult because of the problems involved in collecting click sequences in real application usage. More importantly, accurate predictions are extremely challenging. In this paper, we address these issues. We propose PathFinder, a scheme for collecting click events and based on them predicting the next click in the application. The clicks are collected with the Android Accessibility Service and the next click is predicted via long short-term memory (LSTM). For the prediction, the base click sequence model is first generated from all users' data; then, a personalized model is trained with an individual click sequence. As training data considerably influences the performance of LSTM, several techniques are developed to enhance the quality of the training data. The experimental results for 100 popular applications showed that the coverage and accuracy of click sequence tracing were 95% and 96%, respectively. Furthermore, PathFinder predicted the top three buttons that would be clicked next with a 0.76 F-measure for 1 775 043 real click data.
Bibliographical noteFunding Information:
Manuscript received December 9, 2017; revised April 18, 2018 and July 23, 2018; accepted August 30, 2018. Date of publication September 24, 2018; date of current version May 15, 2019. This work was supported in part by the Samsung Research Funding Center of Samsung Electronics under project SRFCTB1503-02 and in part by the National Research Foundation of Korea grant funded by the Korean Government, Ministry of Education, Science and Technology under Grant NRF-2017M3C4A7083677. This paper was recommended by Associate Editor Z. Yu. (Corresponding author: Hojung Cha.) S. Lee and H. Cha are with the Department of Computer Science, Yonsei University, Seoul 120-749, South Korea (e-mail:, firstname.lastname@example.org; email@example.com).
All Science Journal Classification (ASJC) codes
- Human Factors and Ergonomics
- Control and Systems Engineering
- Signal Processing
- Human-Computer Interaction
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence