基于柏拉图表征假设引导的梦境用户多模态表征用于微视频推荐
Dreaming User Multimodal Representation Guided by The Platonic Representation Hypothesis for Micro-Video Recommendation
AI总结：
本文提出 DreamUMM 方法，基于柏拉图表征假设利用用户历史行为在多模态空间中实时建模用户兴趣，并通过闭式解关联视频偏好与多模态相似度，在数亿日活用户的微视频平台上验证了显著提升用户参与度的效果。
摘要：
在线微视频平台的激增凸显了对先进推荐系统的需求，以缓解信息过载并提供定制化内容。尽管已有诸多进展，但准确且及时地捕捉动态用户兴趣仍然是一项艰巨挑战。受柏拉图表征假设的启发，该假设认为不同数据模态会收敛于一个共享的现实统计模型，我们提出了 DreamUMM（Dreaming User Multi-Modal Representation，梦境用户多模态表征），这是一种利用用户历史行为在多模态空间中创建实时用户表征的新方法。DreamUMM 采用闭式解将用户视频偏好与多模态相似度相关联，假设用户兴趣可以在统一的多模态空间中被有效表示。此外，我们提出了 Candidate-DreamUMM，用于缺乏近期用户行为数据的场景，仅从候选视频中推断用户兴趣。大规模在线 A/B 测试表明，在用户参与指标（包括活跃天数和播放次数）方面取得了显著提升。DreamUMM 在两家拥有数亿日活跃用户的微视频平台上的成功部署，证明了其在个性化微视频内容分发中的实际有效性和可扩展性。我们的工作通过提供实证证据支持用户兴趣表征可以存在于多模态空间中的可能性，为表征收敛的持续探索做出了贡献。
英文摘要：
The proliferation of online micro-video platforms has underscored the necessity for advanced recommender systems to mitigate information overload and deliver tailored content. Despite advancements, accurately and promptly capturing dynamic user interests remains a formidable challenge. Inspired by the Platonic Representation Hypothesis, which posits that different data modalities converge towards a shared statistical model of reality, we introduce DreamUMM (Dreaming User Multi-Modal Representation), a novel approach leveraging user historical behaviors to create real-time user representation in a multimoda space. DreamUMM employs a closed-form solution correlating user video preferences with multimodal similarity, hypothesizing that user interests can be effectively represented in a unified multimodal space. Additionally, we propose Candidate-DreamUMM for scenarios lacking recent user behavior data, inferring interests from candidate videos alone. Extensive online A/B tests demonstrate significant improvements in user engagement metrics, including active days and play count. The successful deployment of DreamUMM in two micro-video platforms with hundreds of millions of daily active users, illustrates its practical efficacy and scalability in personalized micro-video content delivery. Our work contributes to the ongoing exploration of representational convergence by providing empirical evidence supporting the potential for user interest representations to reside in a multimodal space.
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。
