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Sequential UI behaviour prediction system based on long short-term memory networks

Authors
Chung, JihyeHong, SeongjinKang, ShinjinKim, Changhun
Issue Date
2022
Publisher
TAYLOR & FRANCIS LTD
Keywords
Behaviour prediction; UI recommendation; adatpive UI; UI optimisation
Citation
BEHAVIOUR & INFORMATION TECHNOLOGY, v.41, no.6, pp.1258 - 1269
Indexed
SCIE
SSCI
SCOPUS
Journal Title
BEHAVIOUR & INFORMATION TECHNOLOGY
Volume
41
Number
6
Start Page
1258
End Page
1269
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/129979
DOI
10.1080/0144929X.2021.1871954
ISSN
0144-929X
Abstract
In this paper, we propose a method for user interface (UI) behaviour prediction in commercial applications. The proposed method predicts appropriate UI behaviours for an application by learning repeated UI behaviour sequences from users. To this end, we adopted the long short-term memory algorithm based on the evaluation of a keystroke-level model. Our prediction model takes up to seven consecutive actions as inputs to predict the final UI actions that a user is likely to perform. We verified the effectiveness of the proposed method for both PC applications and mobile game environments. Our experimental results demonstrate that the proposed system can predict user UI behaviours in an application on the client side and provide useful behavioural information for optimising UI layouts.
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