.png){kind=icon}
.png){kind=icon}
Return
User intention prediction for trigger-action programming rule using multi-view representation learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2024.126198.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Trigger-action programming (TAP) allows users to create rules for automating smart devices and Internet services, such as If a person is leaving, then turn off the air conditioner. User intention, as the internal driving force behind rule creation, plays a crucial role in understanding user demands. While existing research can easily identify users' explicit demands, such as turning off the air conditioner, it often fails to recognize implicit intentions, such as energy saving. Predicting user intention presents several challenges, including (1) contextual dependencies between triggers and actions that lead to different intentions when users combine various functions, (2) identifying user intention in short function description texts, and (3) entities with similar functions that do not reflect user intentions due to a lack of domain-specific knowledge. To address these challenges, we propose a multi-view representation learning-based framework, MvTAP, jointly in (1) user view, (2) developer view, and (3) knowledge view. In MvTAP, we first design different methods to learn the representation of the three views, respectively. We then propose a transformer-based method to fuse the representations of these views. User intention prediction is formulated as a multi-label classification task. The effectiveness of MvTAP is validated using a real-world IFTTT dataset. Experimental results show that the proposed framework can effectively predict user intentions in TAP rules.
Keywords:
Internet of Things
Trigger-action programming
User intention prediction
Multi-view representation learning
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
Organization
No organization information available