LLM-Alignment Live-Streaming Recommendation
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Apr 2025.
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Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2025MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptationcited by 10
- 2025LLM-Aligned Geographic Item Tokenization for Local-Life Recommendationcited by 3
- 2026QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modelingcited by 2
- 2026Awakening Dormant Users: Generative Recommendation with Counterfactual Functional Role Reasoningcited by 0
- 2026Enhancing Local Life Service Recommendation with Agentic Reasoning in Large Language Modelcited by 0
- 2026Enhancing Local Life Service Recommendation with Agentic Reasoning in Large Language Model15 Apr 2026
- 2026Awakening Dormant Users: Generative Recommendation with Counterfactual Functional Role Reasoning13 Feb 2026
- 2026QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modeling9 Feb 2026
- 2025LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation18 Nov 2025
- 2025MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation21 Aug 2025