# LLM-Alignment Live-Streaming Recommendation

arXiv: https://arxiv.org/abs/2504.05217 · HTML: https://mlsanity.com/paper/2504.05217/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2025.

## Built on by (most cited first, 5 of 5)

- [MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation](https://mlsanity.com/paper/2508.15281/) (2025)
- [LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation](https://mlsanity.com/paper/2511.14221/) (2025)
- [QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modeling](https://mlsanity.com/paper/2602.08559/) (2026)
- [Awakening Dormant Users: Generative Recommendation with Counterfactual Functional Role Reasoning](https://mlsanity.com/paper/2602.13134/) (2026)
- [Enhancing Local Life Service Recommendation with Agentic Reasoning in Large Language Model](https://mlsanity.com/paper/2604.14051/) (2026)

## 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.