连接短视频与直播：推理引导的多模态大语言模型用于跨域表示学习
Bridging Short Videos and Live Streams: Reasoning-Guided Multimodal LLMs for Cross-Domain Representation Learning
AI总结：
针对短视频到直播的跨域推荐冷启动问题，提出推理引导的跨域表示学习框架RGCD-Rep，利用多模态大语言模型生成结构化推理知识并蒸馏到轻量模型，通过两阶段训练学习可迁移的物品表示，在快手部署后显著提升核心业务指标。
摘要：
随着直播服务的发展，许多平台提供短视频和直播以满足多样化需求。短视频承载大量流量和丰富的行为信号，而直播是核心转化场景但行为数据稀疏，导致冷启动问题严重。将用户兴趣从短视频迁移到直播推荐可以缓解这些问题。同时，短视频和直播是复杂的多模态物品，整合多模态信号可提升推荐性能。尽管多模态大语言模型（MLLMs）展现出强大的多模态理解和推理能力，但其在跨域推荐中的应用仍未被充分探索。为此，我们提出推理引导的跨域表示学习（RGCD-Rep），一个从短视频到直播的跨域推荐推理引导框架。RGCD-Rep资源高效地引入MLLM推理，并通过两阶段训练学习由行为协作引导的可迁移物品表示。首先，推理感知蒸馏让冻结的教师MLLM生成结构化跨域推理知识，并将其蒸馏到轻量学生MLLM中。其次，迁移性引导的跨域表示学习将物品表示分解为可迁移表示和领域残差表示。最终表示离线计算并集成到下游检索任务中，实现低成本的工业部署。大量离线实验证明了RGCD-Rep的优越性。在快手直播推荐系统部署后，A/B测试显示多个核心业务指标显著提升，证实了其在真实工业场景中的有效性和实用性。RGCD-Rep已全面部署，每日服务超过4亿用户。
英文摘要：
As live streaming services grow, many platforms offer short videos and live streams to meet diverse needs. Short videos carry substantial traffic and rich behavior signals, whereas live streaming is a core conversion scenario with sparse behavior data, making cold start severe. Transferring user interests from short videos to live streaming recommendation can alleviate these issues. Meanwhile, short videos and live streams are complex multimodal items, and integrating multimodal signals improves recommendation performance. Although Multimodal Large Language Models (MLLMs) show strong multimodal understanding and reasoning, their application to cross-domain recommendation remains underexplored. To this end, we propose Reasoning-Guided Cross-Domain Representation Learning (RGCD-Rep), a reasoning-guided framework for cross-domain recommendation from short videos to live streams. RGCD-Rep introduces MLLM reasoning resource-efficiently and learns transferable item representations guided by behavioral collaboration via two-stage training. First, reasoning-aware distillation lets a frozen teacher MLLM generate structured cross-domain reasoning knowledge and distills it into a lightweight student MLLM. Second, transferability-guided cross-domain representation learning decomposes item representations into transferable and domain residual representations. The resulting representations are computed offline and integrated into downstream retrieval tasks, enabling low-cost industrial deployment. Extensive offline experiments demonstrate RGCD-Rep's superiority. After deployment in Kuaishou's live streaming recommendation system, A/B tests show significant gains across multiple core business metrics, confirming its effectiveness and practicality in real industrial scenarios. RGCD-Rep is fully deployed and serves over 400 million users daily.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
