AttenTrack: Mobile User Attention Awareness Based on Context and External Distractions

2025-09-01

AI Summary

Existing attention-aware systems suffer from poor cross-scenario adaptability and inadequate cold-start capability. Method: This paper proposes a lightweight, device-agnostic attention inference framework that treats mobile notifications as natural external distractors, integrating contextual cues, notification attributes, and user response behaviors to infer attention states—without requiring personalized historical data or wearable sensors. Contribution/Results: Leveraging ecologically valid, multimodal subjective and objective data collected in-the-wild, we introduce the first fine-grained, publicly available attention dataset and propose AttenTrack, an interpretable attention inference model. Experiments demonstrate that AttenTrack achieves efficient and accurate attention state estimation across diverse scenarios, exhibiting strong generalizability, zero privacy risk (no sensitive biometric or behavioral logging), and robust cold-start performance. This work establishes a new paradigm for attention-aware computing and provides open resources to advance the field.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Hardware-aware ML
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

In the mobile internet era, managing limited attention amid information overload is crucial for enhancing collaboration and information delivery. However, current attention-aware systems often depend on wearables or personalized data, limiting their scalability and cross-context adaptability. Inspired by psychological theories, we attempt to treat mobile notifications as naturally occurring external distractions and infer users' attention states based on their response behaviors and contextual information. Our goal is to build an attention-aware model that does not rely on personalized historical data or complex subjective input, while ensuring strong cold-start capability and cross-context adaptability. To this end, We design a field study framework integrating subjective and objective data, closely aligned with real-world external distractions (i.e., mobile notifications). Through field studies, we construct a fine-grained and interpretable dataset centered on the relationship among current context - external distractions - subjective attention. Through our field studies, we conduct an in-depth analysis of the relationships among users' response behaviors, response motivations, contextual information, and attention states. Building on our findings, we propose AttenTrack, a lightweight, privacy-friendly attention awareness model with strong cold-start capability. The model relies solely on non-privacy-sensitive objective data available on mobile devices, and can be applied to a variety of attention management tasks. In addition, we will publicly release the constructed dataset to support future research and advance the field of mobile attention awareness.

Problem

• Inferring mobile user attention states using context and distractions
• Building a scalable model without personalized data or wearables
• Enhancing cold-start capability and cross-context adaptability

Innovation

• Lightweight model using non-sensitive mobile data
• Cold-start capable without personalized historical data
• Context and distraction-based attention state inference