Context modeling and recognition are crucial for adaptive mobile and ubiquitous computing. Context-awareness in mobile environments relies on prompt reactions to context changes. However, current solutions focus on limited context information processed on centralized architectures, risking privacy leakage and lacking personalization. On-device context modeling and recognition are emerging research trends, addressing these concerns. Social interactions and visited locations play significant roles in characterizing daily life scenarios. This paper proposes an unsupervised and lightweight approach to model the user's social context and locations directly on the mobile device. Leveraging the ego-network model, the system extracts high-level, semantic-rich context features from smartphone-embedded sensor data. For the social context, the approach utilizes data on physical and cyber social interactions among users and their devices. Regarding location, it prioritizes modeling the familiarity degree of specific locations over raw location data, such as GPS coordinates and proximity devices. The effectiveness of the proposed approach is demonstrated through three sets of experiments, employing five real-world datasets. These experiments evaluate the structure of social and location ego networks, provide a semantic evaluation of the proposed models, and assess mobile computing performance. Finally, the relevance of the extracted features is showcased by the improved performance of three machine learning models in recognizing daily-life situations. Compared to using only features related to physical context, the proposed approach achieves a 3% improvement in AUROC, 9% in Precision, and 5% in Recall.

翻译：情境建模与识别对于自适应移动计算和普适计算至关重要。移动环境中的情境感知依赖于对情境变化的快速响应。然而，现有方案通常聚焦于有限的情境信息并基于集中式架构处理，这存在隐私泄露风险且缺乏个性化。设备端情境建模与识别作为新兴研究方向，可有效应对上述问题。社交互动与访问地点在刻画日常生活场景中发挥重要作用。本文提出一种无监督且轻量级的方法，直接在移动设备上对用户社交情境和位置进行建模。该系统利用自我中心网络模型，从智能手机嵌入式传感器数据中提取高层次、语义丰富的情境特征。就社交情境而言，该方法利用用户及其设备之间的物理与网络社交互动数据。针对位置建模，该方法优先建模特定场所的熟悉度，而非原始位置数据（如GPS坐标与临近设备）。通过采用五个真实世界数据集开展三组实验，验证了所提方法的有效性。这些实验评估了社交与位置自我网络的结构，对所提模型进行语义评估，并衡量移动计算性能。最后，通过三种机器学习模型在日常生活场景识别任务中的性能提升，展示了所提取特征的相关性。相较于仅使用物理情境相关特征，本方法在AUROC、精确率和召回率上分别提升3%、9%和5%。