Track Co-chairs
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Ziqiong Zhang
Professor
ziqiong@hit.edu.cn
Harbin Institute of Technology
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Jianbin Li
Professor
jbli@mail.hust.edu.cn
Huazhong University of Science and Technology
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Hui Zhu
Associate Professor
zhuhui@gzhu.edu.cn
Guangzhou University
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Chenwei Li
Associate Professor
chenwei.li@xjtlu.edu.cn
Xi'an Jiaotong-Liverpool University
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Hongming Gao
Associate Professor
hmgao@gzhu.edu.cn
Guangzhou University
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Brief Introduction
Digital commerce is undergoing a transformative evolution from conventional models to AI-powered live streaming ecosystems, which feature real-time engagement, dynamic content delivery, and resilient operational frameworks. This track provides a dedicated platform for cutting-edge research exploring how intelligent technologies and adaptive management strategies can enhance performance, sustainability, and competitiveness in the fast-evolving live streaming marketplace. The core focus is twofold: the application of AI and advanced analytics in optimizing live streaming e-commerce operations, and the development of resilience-oriented strategies and frameworks to manage uncertainties such as demand spikes, logistics disruptions, platform policy changes, and consumer sentiment volatility. We focus on research with theoretically grounded insights, empirical validations, or scalable models for integrating AI into live streaming operations while ensuring system robustness and adaptability, exploring the intersection of artificial intelligence, platform economy, consumer psychology, operational agility, and risk management in global live streaming e-commerce.
Topics
- AI-driven optimization of live streaming e-commerce operations (e.g., real-time recommendation systems, automated content generation, virtual hosts, AI-enhanced customer interaction).
- Data-driven analysis of audience behavior, purchase journeys, and social dynamics in live streaming commerce.
- Resilience strategies and frameworks for managing uncertainties in live streaming (e.g., demand surges, logistics disruptions, platform policy changes, market volatility).
- Mining insights from multimodal live streaming data (e.g., real-time comments, voice sentiment, visual focus tracking, and interaction patterns).
- Computational advertising and marketing performance measurement in live streaming e-commerce (e.g., ad placement optimization, real-time ROI analysis, attribution modeling).
- Ethical, governance, and societal implications of AI in live streaming commerce (e.g., consumer privacy, algorithmic transparency, misinformation regulation, digital well-being).
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