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

Jihye Chung, Seongjin Hong, Shinjin Kang, Changhun Kim

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalBehaviour and Information Technology
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Behaviour prediction
  • UI optimisation
  • UI recommendation
  • adatpive UI

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Social Sciences(all)
  • Human-Computer Interaction

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