SHA-SCP: A UI Element Spatial Hierarchy Aware Smartphone User Click Behavior Prediction Method

Predicting user click behavior and making relevant recommendations based on the user’s historical click behavior are critical to simplifying operations and improving user experience. Modeling User Interface (UI) elements is essential to user click behavior prediction, while the complexity and variety of the UI make it difficult to adequately capture the information of different scales. In addition, the lack of relevant datasets also presents difficulties for such studies. In response to these challenges, we construct a fine-grained smartphone usage behavior dataset containing 3 664 325 clicks of 100 users and propose a UI element <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>patial <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</u>ierarchy <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u>ware <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>martphone user <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u>lick behavior <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u>rediction method (SHA-SCP). SHA-SCP builds element groups by clustering the elements according to their spatial positions and uses attention mechanisms to perceive the UI at the element level and the element group level to fully capture the information of different scales. Experiments are conducted on the fine-grained smartphone usage behavior dataset, and the results show that our method outperforms the best baseline by an average of 18.35<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 13.86<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, and 11.97<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, respectively.

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Journal
IEEE Transactions on Human-Machine Systems
Published
2025-09-05
DOI
https://doi.org/10.1109/thms.2025.3601578
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

SHA-SCP: A UI Element Spatial Hierarchy Aware Smartphone User Click Behavior Prediction Method

Kai Qian, Qian Chen, Xiaofan Zhang, Ling Chen et al.
IEEE Transactions on Human-Machine Systems
Human Mobility and Location-Based Analysis
article

SHA-SCP: A UI Element Spatial Hierarchy Aware Smartphone User Click Behavior Prediction Method

Kai Qian, Qian Chen, Xiaofan Zhang, Ling Chen, Hongyu Shi, Yiyi Peng
article en

Abstract

Predicting user click behavior and making relevant recommendations based on the user’s historical click behavior are critical to simplifying operations and improving user experience. Modeling User Interface (UI) elements is essential to user click behavior prediction, while the complexity and variety of the UI make it difficult to adequately capture the information of different scales. In addition, the lack of relevant datasets also presents difficulties for such studies. In response to these challenges, we construct a fine-grained smartphone usage behavior dataset containing 3 664 325 clicks of 100 users and propose a UI element <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>patial <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</u>ierarchy <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u>ware <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>martphone user <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u>lick behavior <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u>rediction method (SHA-SCP). SHA-SCP builds element groups by clustering the elements according to their spatial positions and uses attention mechanisms to perceive the UI at the element level and the element group level to fully capture the information of different scales. Experiments are conducted on the fine-grained smartphone usage behavior dataset, and the results show that our method outperforms the best baseline by an average of 18.35<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 13.86<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, and 11.97<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, respectively.

IEEE Transactions on Human-Machine SystemsVol. 55(6)
NIST Center for Neutron Research (US), Zhejiang University (CN)
Openalex Percentile: Top 76%
Human Mobility and Location-Based Analysis
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