PersoNo: Personalised Notification Urgency Classifier in Mixed Reality

TL;DR

PersoNo is a personalised notification urgency classifier for Mixed Reality that uses user behaviour and activity context to reduce interruptions, achieving over 81% accuracy.

Contribution

This paper introduces PersoNo, the first MR notification dataset and a large language model-based classifier that considers activity context and user behaviour for urgency detection.

Findings

Achieved 81.5% classification accuracy.

Reduced false negative rate to 0.381.

Identified activity context as crucial for notification urgency.

Abstract

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.