CAREER: Leveraging Recommendations for Self-Actualization

The primary research goal of this project is to leverage recommendation technology to support users in developing, exploring, and understanding their preferences based on their long-term goals and ambitions. Recommender systems provide their users with recommendations based on their expressed preferences, but decision scientists have demonstrated that users' preferences are often constructed on the fly and thereby focused on their immediate desires rather than their longer-term goals.

The recommendations, in turn, tend to reflect these short-term likes, ignoring users' ambitions and long-term goals. This proposal will take an important step towards escaping this vicious circle by developing and testing two novel user-adaptive interaction mechanisms that leverage recommendation algorithms to support unique new ways for users to develop, discover, and understand their own preferences.

This will turn the users of these systems into confident consumers who are able to construct preferences that focus on their broader long-term objectives. The decision-support platform and the proposed interaction mechanisms will be made available to academics to support their research on recommender systems and decision-making.

Two innovative interaction mechanisms can advance the field of recommender systems research as well as the field of decision science by extending the reach of personalized decision-support tools beyond "choice" towards preference construction: (1) Personalized preference profiles that use recommendation algorithms to visualize users' preferences and highlight surprising preference dynamics. These profiles will induce preference construction by helping users understand their preferences, reflect upon their long-term goals, and make better-informed decisions. (2) Preference-based communities that use recommendation algorithms to automatically create ad-hoc groups of users whose interests overlap enough to generate interesting discussion, yet not so much that they turn into epistemic bubbles.

These communities seek to induce preference construction through discussion, thereby reinstating the social and intellectual benefits of active advice-giving practices. This project will integrate these interaction mechanisms into live systems, supporting education and outreach activities where students of all ages will use these systems to make decisions based on their preferences and life goals.

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.