Dreamming User Multimodal Representation for Micro-Video Recommendation
<|reference_start|>Dreamming User Multimodal Representation for Micro-Video Recommendation: 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.<|reference_end|>
@article{lin2024dreaming,
    title={Dreaming User Multimodal Representation Guided by The Platonic
  Representation Hypothesis for Micro-Video Recommendation},
    author={Chengzhi Lin, Hezheng Lin, Shuchang Liu, Cangguang Ruan, LingJing Xu,
  Dezhao Yang, Chuyuan Wang, Yongqi Liu},
    journal={arXiv preprint arXiv:2410.03538},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03538},
    primaryClass={cs.IR cs.AI cs.CV}
}