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.
Authors
- Kai Qian
- Qian Chen (ORCID: https://orcid.org/0000-0002-5632-7630)
- Xiaofan Zhang (ORCID: https://orcid.org/0000-0002-1726-1907)
- Ling Chen (ORCID: https://orcid.org/0000-0003-1934-5992)
- Hongyu Shi (ORCID: https://orcid.org/0000-0002-8625-7135)
- Yiyi Peng
Institutions
- NIST Center for Neutron Research (US)
- Zhejiang University (CN)
Publication Details
- 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