CAREER: Structured Output Models of Recommendations, Activities, and Behavior

这个项目将研究人类行为的预测模型，这些模型能够估计丰富的结构化输出。这种预测模型是科学和工业中许多最重要的计算系统的基础，从电子商务到个性化医疗保健。虽然现有的模型(即“推荐系统”)通常专注于简单的预测(用户的下一次点击或星级评级、患者的下一次症状等)，但该项目将开发能够以文本、图像和序列的形式生成输出的模型。这些新的预测建模模式将允许个性化推荐系统适应于回答复杂问题、预测细微差别的反应，甚至设计引起特定反应的新内容。该项目的技术方法将个性化推荐系统的想法与新出现的生成式建模技术相结合。来自推荐系统的想法可以用来处理个性化、主观性或由于个体之间的差异而产生的其他变化等问题；来自生成性建模的想法允许生成复杂的输出(文本、图像、序列)。这一技术贡献既可以被视为能够处理更复杂查询的新形式的推荐系统，也可以被视为一套新的生成性建模方法，可以解释个人之间的差异。该项目将对复杂、高维数据遇到个性化和主观性问题的应用产生影响。要调查的具体例子包括在线活动痕迹、电子商务和个性化健康。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will investigate predictive models of human behavior that are capable of estimating rich, structured outputs. Such predictive models underlie many of the most important computing systems in science and industry, ranging from e-Commerce to personalized healthcare. While existing models (i.e., "recommender systems") typcially focus on simple predictions (a user's next click or star rating, a patient's next symptom, etc.), this project shall develop models capable of generating outputs in the form of text, images, and sequences. These new modalities of predictive modeling will allow personalized recommender systems to be adapted to answer complex questions, predict nuanced reactions, and even to design new content that elicits a certain reaction.The project's technical approach combines ideas from personalized recommender systems with newly-emerging techniques for generative modeling. Ideas from recommender systems can be used to handle issues like personalization, subjectivity, or other variance that arises due to differences between individuals; ideas from generative modeling allow complex outputs (text, images, sequences) to be generated. This technical contribution can be viewed either as a new form of recommender system capable of handling more complex queries, or alternately as a new suite of generative modeling approaches that can account for variance between individuals. This project will have impact to applications where complex, high-dimensional data meets issues of personalization and subjectivity. Specific examples to be investigated include online activity traces, e-Commerce, and personalized health.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.