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

University Of California-San Diego, La Jolla CA

Investigators

Abstract

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.