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Click Sequence Prediction in Android Mobile Applications
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DOI：10.1109/THMS.2018.2868806.png)
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Abstract
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Predicting a click sequence inmobile 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.
Keywords:
Android
click sequence prediction
deep learning
long short-term memory (LSTM)
mobile devices
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