Personalized recommendation requires capturing the complex latent intents underlying user-item interactions. Existing structural models, however, often fail to preserve perspective-dependent interaction semantics and provide only indirect supervision for aligning user and item intents, lacking explicit interaction-level constraints. This entangles heterogeneous interaction signals, leading to semantic ambiguity, reduced robustness under sparse interactions, and limited interpretability. To address these issues, we propose DMICF, a Dual-Perspective Disentangled Multi-Intent framework for collaborative filtering. DMICF models interactions from complementary user- and item-centric perspectives and employs a macro-micro prototype-aware variational encoder to disentangle fine-grained latent intents. Interaction-level supervision enforces dimension-wise alignment between user and item intents, grounding latent factors and enabling their collaborative emergence. Importantly, each component is architecturally flexible, and performance is robust to specific module instantiations. We offer a theoretical analysis to help explain how prototype-aware conditioning may alleviate posterior collapse, while the reconstruction objective promotes intent-wise contrastive alignment between positive and negative interactions. Extensive experiments on multiple benchmarks demonstrate consistent improvements over strong baselines, with ablations validating each core component.

翻译：个性化推荐需要捕捉用户-物品交互背后的复杂潜在意图。然而，现有结构模型通常难以保留视角依赖的交互语义，且仅通过间接监督对齐用户与物品意图，缺乏显式的交互层级约束。这导致异构交互信号纠缠，引发语义歧义、稀疏交互下的鲁棒性降低以及可解释性受限。针对这些问题，我们提出DMICF——一种面向协同过滤的双视角解耦多意图框架。DMICF从互补的用户中心与物品中心视角建模交互，并采用宏微观原型感知变分编码器解耦细粒度潜在意图。交互层级监督强制用户与物品意图的维度对齐，将潜在因子具体化并促使其协作涌现。值得注意的是，每个组件均具有架构灵活性，且对具体模块实例化的鲁棒性良好。我们提供理论分析以阐释原型感知条件化可能缓解后验坍塌的机制，同时重构目标促进正负交互间的意图级对比对齐。在多个基准数据集上的大量实验表明，该方法相较于强基线模型具有一致性能提升，消融实验验证了各核心组件的有效性。