Paper Info
Title
Your Mouse Reveals Your Next Activity: Towards Predicting User Intention From Mouse Interaction
Abstract
This paper presents an investigation into user intention prediction in two common web-based tasks: crowdsourcing annotation and web search, based on human-mouse interaction information. User experience is gaining importance within the research area of human-centered computing, and is particularly useful for complex, multi-step tasks. To enhance user experience, the computer should be intelligent enough to be able to predict the user intention. For instance, an intelligent agent might be able to anticipate when the user is about to press a button, and helpfully enlarge or highlight it in advance. In this paper, we propose two prediction models on user intention: a classical model that considers only historical mouse activity sequence, and a multimodal model that utilizes mouse interaction signals as well as features extracted from mouse trajectory and clicking events. We evaluate our models and find that they achieve reasonable accuracy. Our preliminary results indicate that we can dynamically learn a multimodal model that can effectively predict a user's next activity from historical activity sequence and mouse interaction signals.
Year
DOI
Venue
2017
10.1109/COMPSAC.2017.270
2017 IEEE 41ST ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), VOL 1
Keywords
Field
DocType
User intention, mouse interaction, prediction, multimodal model
Intelligent agent,User experience design,World Wide Web,Annotation,Computer science,Crowdsourcing,Real-time computing,Human–computer interaction,User modeling,Predictive modelling,Interaction information,Trajectory
Conference
ISSN
Citations 
PageRank 
0730-3157
0
0.34
References 
Authors
19
6
Name
Order
Citations
PageRank
Eugene Yujun Fu174.28
Tiffany C. K. Kwok2272.83
Erin You Wu300.34
Hong V. Leong47246.21
Grace Ngai588289.27
Stephen C. F. Chan616815.78