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LLM-Alignment Live-Streaming Recommendation
April 07, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Yueyang Liu, Jiangxia Cao, Shen Wang, Shuang Wen, Xiang Chen, Xiangyu Wu, Shuang Yang, Zhaojie Liu, Kun Gai, Guorui Zhou
arXiv ID
2504.05217
Category
cs.IR: Information Retrieval
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In recent years, integrated short-video and live-streaming platforms have gained massive global adoption, offering dynamic content creation and consumption. Unlike pre-recorded short videos, live-streaming enables real-time interaction between authors and users, fostering deeper engagement. However, this dynamic nature introduces a critical challenge for recommendation systems (RecSys): the same live-streaming vastly different experiences depending on when a user watching. To optimize recommendations, a RecSys must accurately interpret the real-time semantics of live content and align them with user preferences.
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