LLM-Alignment Live-Streaming Recommendation
Clicks: 31
ID: 283538
2025
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Emerging Content
2.7
/100
9 views
9 readers
Trending
AI Quality Assessment
Not analyzed
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.
| Reference Key |
zhou2025llmalignment
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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 |
| Journal | arXiv |
| Year | 2025 |
| DOI |
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