面向联邦冷启动推荐的个性化多视图表示
Personalized and Multi-View Representation for Federated Cold-Start Recommendation
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中文总结 AI 辅助
针对联邦冷启动推荐的双重约束与三类局限，提出PMFRec方法，通过个性化生成器、多视图编码器及知识融合提升冷项目推荐性能与公平性、适应性及鲁棒性。
中文摘要 AI 辅助
联邦推荐（FedRec）可在不集中用户交互历史的情况下实现个性化建模，但现有多数方法假设项目池固定，忽视了新项目持续到达的实际冷项目场景。在双重约束下，服务器无法访问客户端交互数据，客户端无法访问服务器专有项目属性特征，现有联邦冷启动推荐方法存在三类结构性局限：缺乏个性化、因将异质语义强制嵌入单一嵌入空间导致的组合性失效，以及因显式对齐协作与属性表示导致的训练与通信效率低下。为应对这些挑战，本文提出面向联邦冷启动推荐的个性化多视图表示方法（PMFRec）。PMFRec学习个性化表示生成器，从属性特征生成用户特定的项目表示；引入带项目自适应门控与正交性目标的全局多视图编码器，以捕获互补语义视图并减少跨视图冗余。此外，PMFRec将协作与属性知识融合为单一交换项目表示，无需显式客户端正则化器，降低通信开销。在真实世界数据集上的大量实验表明，PMFRec在冷项目推荐中始终优于强基线，还进一步提升了用户级公平性、暖场景适应性及本地差分隐私（LDP）下的鲁棒性。
英文摘要
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).
