Towards Understanding Human Emotional Fluctuations with Sparse Check-In Data
This addresses the challenge of data sparsity in AI tools for domains like self-reported mood tracking, enabling more effective personalization despite limited user input.
The paper tackled the problem of predicting human emotional states from sparse check-in data by proposing a novel probabilistic framework with user-centric feedback-based learning, achieving 60% accuracy in predicting among 64 options compared to a chance level of about 1.56%.
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.