Generative Flow Networks for Personalized Multimedia Systems: A Case Study on Short Video Feeds
Jin, Yili Pan, Ling Zhang, Rui-Xiao Liu, Jiangchuan Liu, Xue
Jin, Yili
Pan, Ling
Zhang, Rui-Xiao
Liu, Jiangchuan
Liu, Xue
Author
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Multimedia systems underpin modern digital interactions, facilitating seamless integration and optimization of resources across diverse multimedia applications. To meet growing personalization demands, multimedia systems must efficiently manage competing resource needs, adaptive content, and user-specific data handling. This paper introduces Generative Flow Networks (GFlowNets, GFNs) as a brave new framework for enabling personalized multimedia systems. By integrating multi-candidate generative modeling with flow-based principles, GFlowNets offer a scalable and flexible solution for enhancing user-specific multimedia experiences. To illustrate the effectiveness of GFlowNets, we focus on short video feeds, a multimedia application characterized by high personalization demands and significant resource constraints, as a case study. Our proposed GFlowNet-based personalized feeds algorithm demonstrates superior performance compared to traditional rule-based and reinforcement learning methods across critical metrics, including video quality, resource utilization efficiency, and delivery cost. Moreover, we propose a unified GFlowNet-based framework generalizable to other multimedia systems, highlighting its adaptability and wide-ranging applicability. These findings underscore the potential of GFlowNets to advance personalized multimedia systems by addressing complex optimization challenges and supporting sophisticated multimedia application scenarios.
Citation
Y. Jin, H. Kong, R.-X. Zhang, J. Liu, X. Liu, and L. Pan, “Generative Flow Networks for Personalized Multimedia Systems: A Case Study on Short Video Feeds,” Proceedings of the 33rd ACM International Conference on Multimedia, pp. 12218–12226, Oct. 2025, doi: 10.1145/3746027.3758147
Source
MM '25: Proceedings of the 33rd ACM International Conference on Multimedia
Conference
The 33rd ACM International Conference on Multimedia
Keywords
Multimedia Systems, Personalization, Generative Flow Networks, Short Video Feeds
Subjects
Source
The 33rd ACM International Conference on Multimedia
Publisher
Association for Computing Machinery