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arXiv Machine Learning By Jaehyung Lim, Wonbin Kweon, Woojoo Kim, Junyoung Kim, Dongha Kim, Hwanjo Yu

Personalized and Multi-View Representation for Federated Cold-Start Recommendation

Read the original on arXiv Machine Learning →

The paper introduces PMFRec, a federated cold-start recommendation framework that addresses personalization, compositionality, and communication inefficiencies. PMFRec generates user-specific item representations from attribute features, employs a global multi-view encoder with adaptive gating and orthogonality to capture complementary semantics, and fuses collaborative and attribute knowledge into a single exchanged representation. Experiments on real-world datasets demonstrate that PMFRec outperforms strong baselines in cold-item recommendation while improving user-level fairness, warm-scenario adaptability, and robustness under Local Differential Privacy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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