Group Recommendation

Individual preferences become diverse—and often conflicting—when people make decisions together. My research develops group recommendation methods that balance personal interests, social relationships, and feedback from multiple participants rather than optimizing for a single user.

Applications include configuring shared virtual-reality shopping experiences and recommending multi-streaming activities. The goal is to find choices that work well for the group while preserving the preferences that matter to each member.