Recommender systems rely on user preferences and item attributes to provide personalized services, yet they inherently face a tension between model accuracy and data privacy. Vertical Federated Learning (VFL) allows multiple organizations to jointly train models without sharing raw data, typically using Private Set Intersection (PSI) to align overlapping users. However, conventional PSI supports only exact identifier matching and fails to accommodate the feature-based conditional filtering frequently required in modern recommendation pipelines. To overcome this limitation, we propose P \(^2\) FR-VFL, a VFL framework that integrates Privacy-Preserving Feature-Retrieval PSI (P \(^2\) FR-PSI). This enhanced PSI mechanism enables participants to privately align and selectively filter user identifiers according to hidden predicate conditions, without disclosing any sensitive feature information. This design enables P \(^2\) FR-VFL to provide a more expressive and privacy-preserving preprocessing stage for federated recommendation tasks. Building on this capability, P \(^2\) FR-VFL also offers a scalable and privacy-enhanced solution for cross-domain recommender systems, effectively reconciling model utility with strong user privacy guarantees in federated environments. Experiments on real-world datasets show that P \(^2\) FR-VFL achieves predictive accuracy comparable to plaintext VFL training, while incurring only \(20\%\) additional communication overhead relative to state-of-the-art PSI schemes, all while supporting flexible alignment capabilities.