Modern ranking systems must comply with emerging AI regulations while resisting manipulation attacks. The EU AI Act’s prohibition on individual user scoring creates a critical gap: reputation-based systems violate compliance requirements, while user-agnostic approaches lack bribery resistance, and both remain vulnerable to demographic bias. We propose a user-agnostic multipartite ranking framework addressing regulatory compliance, security, personalization, and bias. Our approach clusters users by rating patterns and applies localized statistical filtering to remove anomalous ratings, eliminating individual profiling while preserving personalization and enhancing manipulation resistance. Evaluation across three datasets shows substantial bribery resistance improvements, with profitable attacks in only 7 of 18 scenarios versus 8–11 for state-of-the-art baselines. The framework achieves a demographic bias reduction by a factor of 100 compared to a user-agnostic bipartite approach, while avoiding individual user scoring as prohibited by the EU AI Act. Robustness analysis reveals enhanced spam resistance on two datasets, with computational overhead as the primary trade-off.