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LLM-Alignment Live-Streaming Recommendation

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arxiv 2504.05217 v1 pith:OQ6JNXRB submitted 2025-04-07 cs.IR

Yueyang Liu , Jiangxia Cao , Shen Wang , Shuang Wen , Xiang Chen , Xiangyu Wu , Shuang Yang , Zhaojie Liu
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Kun Gai Guorui Zhou
This is my paper
classification cs.IR
keywords live-streamingcontentdynamicreal-timerecommendationrecsysuseraccurately
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. KuaiLive-M3: A Multi-Modal, Multi-Domain, and Multi-Feedback Dataset for Live Streaming Recommendation

    cs.IR 2026-07 conditional novelty 6.0 of 10

    KuaiLive-M3 releases multi-domain Kuaishou logs, ~88M segment multi-modal embeddings, and 25k questionnaires, with benchmarks showing gains from cross-domain transfer, temporal modeling, and sparse explicit feedback.

  2. Towards Generalizable Safety in Crowd Navigation via Conformal Uncertainty Handling

    cs.RO 2025-08 unverdicted novelty 5.0 of 10

    A crowd navigation method augmenting reinforcement learning with conformal uncertainty estimates is claimed to cut collisions under distribution shift, but the manuscript body is an unrelated live streaming dataset paper.

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