基于多尺度时序建模与稀疏专家混合的感知漂移多模态用户表示学习

中文摘要 AI 辅助

从含噪且随时间演变的社交媒体行为中理解用户偏好是一项根本挑战，原因在于兴趣漂移——用户偏好会随时间变化，且呈现多尺度时序模式与多样共存兴趣。为解决该问题，我们提出DUMoE，这是一个感知漂移的多模态用户表示学习统一框架。该模型包含两部分：（i）时序动态感知骨干网络，用于捕获并整合静态用户画像、短期行为信号与长期依赖关系，形成一致表示；（ii）稀疏专家混合（MoE）兴趣适配器，通过专家专业化与自适应路由分解多个潜在兴趣。每个专家建模不同的兴趣子空间，门控网络为每位用户动态选择并聚合相关专家的稀疏子集。为实现稳定有效的优化，我们进一步引入三阶段训练策略，将骨干网络学习、专家专业化与门控优化解耦。在真实社交媒体数据集上的大量实验表明，DUMoE在用户兴趣预测与交互预测任务中均始终优于现有最优方法。

英文摘要

Understanding user preferences from noisy and temporally evolving social media behaviors is fundamentally challenging due to interest drift, where user preferences shift across time and exhibit both multi-scale temporal patterns and diverse co-existing interests. To address this, we propose DUMoE, a unified framework for drift-aware multimodal user representation learning. Our model consists of (i) a temporal dynamics-aware backbone that captures and integrates static profiles, short-term behavioral signals, and long-term dependencies into a coherent representation, and (ii) a sparse mixture-of-experts (MoE) interest adapter that disentangles multiple latent interests via expert specialization and adaptive routing. Each expert models a distinct interest subspace, while a gating network dynamically selects and aggregates a sparse subset of relevant experts for each user. To enable stable and effective optimization, we further introduce a three-stage training strategy that decouples backbone learning, expert specialization, and gating optimization. Extensive experiments on real-world social media datasets show that DUMoE consistently outperforms state-of-the-art methods on both user interest prediction and interaction prediction tasks.

发表机构

• Shanghai Innovation Institute（上海创新研究院）
• Tongji University（同济大学）

机构由 AI 辅助整理，请以论文原文为准。