LLM-Alignment Live-Streaming Recommendation

• URL: http://arxiv.org/abs/2504.05217v1
• Date: Mon, 07 Apr 2025 16:04:00 GMT
• Title: LLM-Alignment Live-Streaming Recommendation
• Authors: Yueyang Liu, Jiangxia Cao, Shen Wang, Shuang Wen, Xiang Chen, Xiangyu Wu, Shuang Yang, Zhaojie Liu, Kun Gai, Guorui Zhou,
• Abstract summary: Integrated short-video and live-streaming platforms have gained massive global adoption, offering dynamic content creation and consumption.<n>The same live-streaming vastly different experiences depending on when a user watching.<n>To optimize recommendations, a RecSys must accurately interpret the real-time semantics of live content and align them with user preferences.
• Score: 20.817796284487468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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