LLM-Alignment Live-Streaming Recommendation

Yueyang Liu; Jiangxia Cao; Shen Wang; Shuang Wen; Xiang Chen; Xiangyu Wu; Shuang Yang; Zhaojie Liu; Kun Gai; Guorui Zhou

LLM-Alignment Live-Streaming Recommendation

Information Retrieval 2025-04-08 v1

Authors: Yueyang Liu , Jiangxia Cao , Shen Wang , Shuang Wen , Xiang Chen , Xiangyu Wu , Shuang Yang , Zhaojie Liu , Kun Gai , Guorui Zhou

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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.

Keywords

video streaming recommendation system recommender system

Cite

@article{arxiv.2504.05217,
  title  = {LLM-Alignment Live-Streaming Recommendation},
  author = {Yueyang Liu and Jiangxia Cao and Shen Wang and Shuang Wen and Xiang Chen and Xiangyu Wu and Shuang Yang and Zhaojie Liu and Kun Gai and Guorui Zhou},
  journal= {arXiv preprint arXiv:2504.05217},
  year   = {2025}
}

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LLM-Alignment Live-Streaming Recommendation

Abstract

Keywords

Cite

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