Research | Hsu-Chao Lai (賴旭昭)

Group Recommendation

Mon, 01 Jan 0001 00:00:00 +0000

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.

Social Influence in Networks

Mon, 01 Jan 0001 00:00:00 +0000

Online behavior is shaped by interaction. My research studies how streamer–viewer exchanges—such as donations and responses—and viewer–viewer relationships influence interests, engagement, and content over time.

By modeling these feedback loops, we can understand the co-evolution of communities and content, then build recommendations that respond to changing social dynamics rather than treating users as isolated individuals.

Team Sports Intelligence

Mon, 01 Jan 0001 00:00:00 +0000

Movement in team sports is not an individual action: every decision depends on teammates, opponents, and the evolving rally. My research models these interactions to forecast player movement and reveal coordinated patterns in doubles badminton.

The broader goal is to turn tracking data into tactical insight—supporting performance analysis, strategy design, and a clearer understanding of how teams create and respond to space.

Intelligent Decision Support

Mon, 01 Jan 0001 00:00:00 +0000

Effective decision support must be technically sound and practical for the people it serves. In healthcare and long-term care, this means combining professional recommendations with each person’s lifestyle, preferences, and everyday constraints.

My research explores data-driven systems for real-world decisions, including gait assessment, power-efficient sleep-stage monitoring, and uncertainty-aware planning. The aim is AI that supports experts while remaining responsive to individual needs.

Learning in Dynamic Markets

Mon, 01 Jan 0001 00:00:00 +0000

Markets evolve continuously under uncertainty. My research develops learning-based strategies that adapt to changing conditions, imperfect predictions, and delayed or partially observed outcomes.

Applications include real-time bidding in online advertising and pairs trading in financial markets, with an emphasis on robust decisions, structural changes, and efficient use of market feedback.