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

• URL: http://arxiv.org/abs/2608.27826v1
• Date: Fri, 28 Aug 2026 01:48:50 GMT
• Title: Personalized and Multi-View Representation for Federated Cold-Start Recommendation
• Abstract summary: We propose Personalized and Multi-view Representation for Federated Cold-Start Recommendation (PMFRec)<n>PMFRec learns a personalized representation generator to produce user-specific item representations from attribute features.<n>Experiments on real-world datasets show that PMFRec consistently outperforms strong baselines in cold-item recommendation.
• Score: 18.117610268256005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated recommendation (FedRec) enables personalized modeling without centralizing users' interaction histories, but most existing methods assume a fixed item pool and thus overlook the practical cold-item setting where new items continuously arrive. Under the dual-sided constraint, where the server cannot access clients' interactions while clients cannot access the server's proprietary item attribute features, prior federated cold-start recommendation approaches suffer from three structural limitations: a lack of personalization, compositionality failure caused by forcing heterogeneous semantics into a single embedding space, and training- and communication-inefficiency arising from explicit alignment between separate collaborative and attribute representations. To address these challenges, we propose Personalized and Multi-view Representation for Federated Cold-Start Recommendation (PMFRec). PMFRec learns a personalized representation generator to produce user-specific item representations from attribute features, and introduces a global multi-view encoder with item-adaptive gating and an orthogonality objective to capture complementary semantic views while reducing cross-view redundancy. In addition, PMFRec fuses collaborative and attribute knowledge into a single exchanged item representation, eliminating the need for an explicit client-side regularizer and reducing communication overhead. Extensive experiments on real-world datasets show that PMFRec consistently outperforms strong baselines in cold-item recommendation and further improves user-level fairness, warm-scenario adaptability, and robustness under Local Differential Privacy (LDP).

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