PersoNo: Personalised Notification Urgency Classifier in Mixed Reality
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911125035024384 |
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| author | Zheng, Jingyao Weng, Haodi Wang, Xian Cui, Chengbin Mayer, Sven Tai, Chi-lok Lee, Lik-Hang |
| author_facet | Zheng, Jingyao Weng, Haodi Wang, Xian Cui, Chengbin Mayer, Sven Tai, Chi-lok Lee, Lik-Hang |
| contents | 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 self-labelled 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19622 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PersoNo: Personalised Notification Urgency Classifier in Mixed Reality Zheng, Jingyao Weng, Haodi Wang, Xian Cui, Chengbin Mayer, Sven Tai, Chi-lok Lee, Lik-Hang Human-Computer Interaction Multimedia 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 self-labelled 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. |
| title | PersoNo: Personalised Notification Urgency Classifier in Mixed Reality |
| topic | Human-Computer Interaction Multimedia |
| url | https://arxiv.org/abs/2508.19622 |