TY - GEN
T1 - PersoNo: Personalised Notification Urgency Classifier in Mixed Reality
AU - Zheng, Jingyao
AU - Weng, Haodi
AU - Wang, Xian
AU - Cui, Chengbin
AU - Mayer, Sven
AU - Tai, Chi Lok
AU - Lee, Lik Hang
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/10
Y1 - 2025/10
N2 - Mixed Reality (MR) is increasingly integrated into daily life, providing enhanced capabilities across various domains. However, users face growing notification streams that disrupt their immersive experience. We present PersoNo, a personalised notification urgency classifier for MR that intelligently classifies notifications based on individual user preferences. Through a user study (N=18), we created the first MR notification dataset containing both selflabelled and interaction-based data across activities with varying cognitive demands. Our thematic analysis revealed that, unlike in mobiles, the activity context is equally important as the content and the sender in determining notification urgency in MR. Leveraging these insights, we developed PersoNo using large language models that analyse users' replying behaviour patterns. Our multi-agent approach achieved 81.5% accuracy and significantly reduced false negative rates (0.381) compared to baseline models. PersoNo has the potential not only to reduce unnecessary interruptions but also to offer users understanding and control of the system, adhering to Human-Centered Artificial Intelligence design principles.
AB - Mixed Reality (MR) is increasingly integrated into daily life, providing enhanced capabilities across various domains. However, users face growing notification streams that disrupt their immersive experience. We present PersoNo, a personalised notification urgency classifier for MR that intelligently classifies notifications based on individual user preferences. Through a user study (N=18), we created the first MR notification dataset containing both selflabelled and interaction-based data across activities with varying cognitive demands. Our thematic analysis revealed that, unlike in mobiles, the activity context is equally important as the content and the sender in determining notification urgency in MR. Leveraging these insights, we developed PersoNo using large language models that analyse users' replying behaviour patterns. Our multi-agent approach achieved 81.5% accuracy and significantly reduced false negative rates (0.381) compared to baseline models. PersoNo has the potential not only to reduce unnecessary interruptions but also to offer users understanding and control of the system, adhering to Human-Centered Artificial Intelligence design principles.
KW - Human Centered Artificial Intelligence
KW - Mixed Reality
KW - Notification Classifier
UR - https://www.scopus.com/pages/publications/105025021351
U2 - 10.1109/ISMAR67309.2025.00112
DO - 10.1109/ISMAR67309.2025.00112
M3 - Conference article published in proceeding or book
AN - SCOPUS:105025021351
T3 - Proceedings - 2025 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2025
SP - 1053
EP - 1063
BT - Proceedings - 2025 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2025
A2 - Eck, Ulrich
A2 - Lee, Gun
A2 - Plopski, Alexander
A2 - Smith, Missie
A2 - Sun, Qi
A2 - Tatzgern, Markus
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2025
Y2 - 8 October 2025 through 12 October 2025
ER -