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arXiv Machine Learning By Ziqing Qian, Haohang Chen, Shengqi Dang, Yuhan Xiong, Canyu Shen, Jiaying Lei, Nan Cao

Drift-Aware Multimodal User Representation Learning via Multi-Scale Temporal Modeling and Sparse Mixture-of-Experts

Read the original on arXiv Machine Learning →

The paper introduces DUMoE, a drift‑aware multimodal user representation framework that models user preferences over time by integrating static profiles, short‑term signals, and long‑term dependencies. It employs a sparse mixture‑of‑experts interest adapter, where each expert captures a distinct latent interest and a gating network selects relevant experts for each user. A three‑stage training strategy decouples backbone learning, expert specialization, and gating optimization, and experiments on real social media data demonstrate that DUMoE outperforms existing methods in user interest and interaction prediction.

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