用少量用户打卡数据精准预测情绪波动，突破数据稀疏瓶颈
Towards Understanding Human Emotional Fluctuations with Sparse Check-In Data
- 基于用户反馈构建概率框架，实现小样本下的个性化情绪预测
- 在64种情绪中达到60%准确率，显著超越随机猜测（1/64）
- 无需大规模数据集，适合医疗、心理健康等高互动低数据场景
数据稀疏性是限制人工智能在多个领域应用的关键挑战，尤其在需要用户主动输入而非自动传感器测量的领域更为突出。在自我报告情绪打卡等需用户主动参与的场景中，持续捕捉情绪状态至关重要，而稀疏数据会阻碍对情绪成因、触发因素等细微差异的把握。现有缓解数据稀缺的方法多依赖启发式规则或大型成熟数据集，倾向于使用难以适应新领域的深度学习模型。本文提出一种新颖的概率框架，融合以用户为中心的反馈学习机制，即使在数据有限的情况下也能实现个性化预测。该方法在64种情绪状态中实现60%的预测准确率（随机猜测为1/64），有效缓解数据稀疏问题，并具备跨应用场景的通用性，弥合了理论研究与实际部署之间的鸿沟。
原文摘要 · Abstract (English)
Data sparsity is a key challenge limiting the power of AI tools across various domains. The problem is especially pronounced in domains that require active user input rather than measurements derived from automated sensors. It is a critical barrier to harnessing the full potential of AI in domains requiring active user engagement, such as self-reported mood check-ins, where capturing a continuous picture of emotional states is essential. In this context, sparse data can hinder efforts to capture the nuances of individual emotional experiences such as causes, triggers, and contributing factors. Existing methods for addressing data scarcity often rely on heuristics or large established datasets, favoring deep learning models that lack adaptability to new domains. This paper proposes a novel probabilistic framework that integrates user-centric feedback-based learning, allowing for personalized predictions despite limited data. Achieving 60% accuracy in predicting user states among 64 options (chance of 1/64), this framework effectively mitigates data sparsity. It is versatile across various applications, bridging the gap between theoretical AI research and practical deployment.
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