This work strives to create a future in which hyper-personalized content, digital services, and personal information management tools let individuals benefit from the data they generate more directly, selectively, and transparently. Individuals will then be empowered to gain insights into their own behavior, personalize their own experiences, and ultimately more effectively utilize the services to achieve their goals. Moreover, the systems that engage end-users with the data they generate can promote local processing and selective sharing of personal information. Given the pervasiveness of online tools in a person's work, personal, and social life, the actions they take are increasingly shaped by recommendation systems. Today's approaches to personalization and recommendation are provider centric. Society as a whole will benefit from broader exploration of personalization and recommendation from the consumers' perspective. Also along these lines, there is increasing concern about the shifts in expectations for individual control over data sharing. This project's user-centered and personal-sharing-policy-aware design is a potential solution to address this tension between providers and users, allowing people to more directly benefit from their data.
The research objective is to develop novel user modeling techniques, policy-aware systems, and rich user interactions that allow individuals to harness their own diverse digital traces ("small data"), enable novel applications, and receive more personally-relevant recommendations while limiting privacy exposure. This research will contribute the novel user modeling and interaction techniques needed to put the individual at the center of their personalization, in particular: (1) Immersive user modeling techniques that analyze diverse types of user data, including social media streams, private text communications, web browsing, geo-location traces, and personal images, to incorporate users' diverse and idiosyncratic interests. (2) Novel recommendation models and policy-aware software architecture that consists of open source building blocks designed to facilitate generalization of this approach to ingest diverse personal data traces and feed diverse application targets. (3) Methods to understand and address key human-centered challenges in immersive recommender systems through participatory design of the user experience, as well as qualitative and quantitative evaluation of deployed systems and applications.
