Sequential UI behaviour prediction system based on long short-term memory networks
- Authors
- Chung, Jihye; Hong, Seongjin; Kang, Shinjin; Kim, 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.