Abstract
User experience could be enhanced if the computer could understand human interaction intention. For instance, it could react to intercept and prevent interaction errors. This paper presents an approach to predicting users' intention in interaction tasks based on past mouse movements. We adopt a long short-term memory (LSTM) model to predict the users' intention via their next mouse click interaction, upon being trained with past mouse interaction behaviors. To evaluate, we consider two scenarios in daily computer usage: a more structured crowdsourcing annotation task and a more free-form, open-ended web search task. Our results indicate that we could predict the next interaction event with reasonable accuracy. We also conducted a pilot study to investigate the possibility of applying our model for nonintentional mouse click detection. We believe that our findings would be beneficial towards the development of better intelligent agents.
Original language | English |
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Title of host publication | IUI 2018 - Proceedings of the 23rd International Conference on Intelligent User Interfaces |
Publisher | Association for Computing Machinery |
Pages | 397-401 |
Number of pages | 5 |
ISBN (Electronic) | 9781450349451 |
DOIs | |
Publication status | Published - 5 Mar 2018 |
Event | 23rd ACM International Conference on Intelligent User Interfaces, IUI 2018 - Tokyo, Japan Duration: 7 Mar 2018 → 11 Mar 2018 |
Conference
Conference | 23rd ACM International Conference on Intelligent User Interfaces, IUI 2018 |
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Country/Territory | Japan |
City | Tokyo |
Period | 7/03/18 → 11/03/18 |
Keywords
- Human-computer interaction
- Mouse interaction
- Non-intentional mouse click detection
- User intention
ASJC Scopus subject areas
- Software
- Human-Computer Interaction