STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data
STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data
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DOI：
10.1145/3486622.3493967
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发表时间：
2021-12
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影响因子：
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通讯作者：
Chunpai Wang;Shaghayegh Sherry Sahebi;Helma Torkamaan
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文献类型：
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作者：
Chunpai Wang;Shaghayegh Sherry Sahebi;Helma Torkamaan
压力水平建模和预测对于向个人推荐活动和干预措施至关重要。虽然在文献中已经提出了成功的压力模型，但用户参与行为，对活动的兴趣和他们的压力水平之间仍然缺乏联系。本文提出了一种新的多视图张量分解方法，用于异构数据的压力和用户行为建模，该方法可以提供个性化的压力跟踪和跨时间的合理用户行为建模。据我们所知，这是第一种可以同时使用多种数据资源（如压力测量，活动评级和参与度）对用户压力和行为进行建模的方法。我们的实验表明，利用多个数据资源不仅可以提高稀疏数据的预测，还可以发现潜在的压力-活动模式。我们证明了我们提出的模型的有效性，通过一个独立的压力管理移动的应用程序收集的数据集。
Stress level modeling and predictions are essential in recommending activities and interventions to individuals. While successful stress models have been proposed in the literature, there is still a missing connection between user engagement behaviors, interest in activities, and their stress levels. In this paper, we propose a novel multi-view tensor decomposition method for stress and user behavior modeling with heterogeneous data, which could provide personalized stress tracking and plausible user behavior modeling across time. To the best of our knowledge, it is the first method that could model user stress and behavior at the same time with multiple resources of data, such as stress measurement, activity rating, and engagement. Our experiments show that leveraging multiple resources of data could not only improve predictions with sparse data, but also results in discovering the underlying stress-activity patterns. We demonstrate the effectiveness of our proposed model on the dataset collected via a self-contained stress management mobile application.