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Personalized and Explainable Models of Text and Sequences


Understanding and modeling heterogeneous human interaction data is essential to build next-generation natural (e.g. conversational, personalized) interactive systems. Textual data (e.g. dialogues, product reviews), relational data (e.g. knowledge bases) and behavioral data (e.g. purchases, clicks) are ubiquitous sources of data describing human interactions. Recent advances in natural language processing and human behavior modeling have led to drastic development in interactive systems as recommender systems, search engines, conversational agents, fitness trackers, etc. However, it remains challenging for such systems to provide personalized and explainable outputs, which are critical to improving the user experience.

In this thesis, we aim to study two main sources of human interaction data, i.e., text and sequences, to improve behavioral prediction, enhance model interpretability, and increase model usability. In particular, my work focuses on studying personalized models to estimate and interpret user interactions and exploring explainable models to understand and learn from user-generated text. The primary contributions are 1) a personalized text generation model which can facilitate assistive writing; 2) a personalized model that generates textual justification for modern recommender systems; 3) a personalized algorithm that models workout activity sequences, predicts user heart rate profile and provides fitness-aware recommendations; 4) an algorithm that learns to attend on essential terms in complex questions and retrieves relevant evidence to boost performance in open-domain question answering; 5) a visual-semantic embedding based method to report abnormal findings on chest X-rays.

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