Large language models (LLMs) have become an important semantic infrastructure for modern recommender systems. A prevailing paradigm integrates LLM-derived semantic embeddings with collaborative representations via representation alignment, implicitly assuming that the two views encode a shared latent entity and that stronger alignment yields better results. We formalize this assumption as the global low-complexity alignment hypothesis and argue that it is stronger than necessary and often structurally mismatched with real-world recommendation settings. We propose a complementary perspective in which semantic and collaborative representations are treated as partially shared yet fundamentally heterogeneous views, each containing both shared and view-specific factors. Under this shared-plus-private latent structure, enforcing global geometric alignment may distort local structure, suppress view-specific signals, and reduce informational diversity. To support this perspective, we develop complementarity-aware diagnostics that quantify overlap, unique-hit contribution, and theoretical fusion upper bounds. Empirical analyses on sparse recommendation benchmarks reveal low item-level agreement between semantic and collaborative views and substantial oracle fusion gains, indicating strong complementarity. Furthermore, controlled alignment probes show that low-capacity mappings capture only shared components and fail to recover full collaborative geometry, especially under distribution shift. These findings suggest that alignment should not be treated as the default integration principle. We advocate a shift from alignment-centric modeling to complementarity fusion-centric, complementarity-aware design, where shared factors are selectively integrated while private signals are preserved. This reframing provides a principled foundation for the next generation of LLM-enhanced recommender systems.

翻译：大型语言模型（LLMs）已成为现代推荐系统的重要语义基础设施。一种主流范式通过表示对齐将LLM衍生的语义嵌入与协同表示进行整合，其隐含假设是两种视图编码了共享的潜在实体，且对齐越强结果越好。我们将这一假设形式化为全局低复杂度对齐假设，并认为其不仅强于实际需要，而且常常在结构上与真实世界的推荐场景不符。我们提出一种互补视角，将语义表示和协同表示视为部分共享但本质上异质的视图，每种视图均包含共享因子和特定视图因子。在这种共享加私有潜在结构下，强制进行全局几何对齐可能扭曲局部结构、抑制视图特定信号并降低信息多样性。为支持这一视角，我们开发了互补性感知诊断方法，用于量化重叠度、唯一命中贡献和理论融合上界。在稀疏推荐基准上的实证分析显示，语义视图与协同视图之间存在低的项目级一致性，以及显著的神谕融合增益，这表明了强烈的互补性。此外，受控对齐探测表明，低容量映射仅能捕获共享成分，无法恢复完整的协同几何结构，尤其是在分布偏移情况下。这些发现表明，对齐不应被视为默认的整合原则。我们倡导从以对齐为中心的建模转向以互补性融合为中心、互补性感知的设计，在此框架下选择性地整合共享因子，同时保留私有信号。这一重构为下一代LLM增强型推荐系统提供了原则性基础。