Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.g., views, clicks, purchases) to enrich preference modeling and alleviate data sparsity issues in traditional single-behavior approaches. However, existing MBR methods face fundamental challenges: they lack principled frameworks to model complex confounding effects from user behavioral habits and item multi-behavior distributions, struggle with effective aggregation of heterogeneous auxiliary behaviors, and fail to align behavioral representations across semantic gaps while accounting for bias distortions. To address these limitations, we propose MCLMR, a novel model-agnostic causal learning framework that can be seamlessly integrated into various MBR architectures. MCLMR first constructs a causal graph to model confounding effects and performs interventions for unbiased preference estimation. Under this causal framework, it employs an Adaptive Aggregation module based on Mixture-of-Experts to dynamically fuse auxiliary behavior information and a Bias-aware Contrastive Learning module to align cross-behavior representations in a bias-aware manner. Extensive experiments on three real-world datasets demonstrate that MCLMR achieves significant performance improvements across various baseline models, validating its effectiveness and generality. All data and code will be made publicly available. For anonymous review, our code is available at the following the link: https://github.com/gitrxh/MCLMR.

翻译：多行为推荐利用多种用户交互类型（如浏览、点击、购买）来丰富偏好建模，并缓解传统单行为方法中的数据稀疏性问题。然而，现有MBR方法面临根本性挑战：缺乏原则性框架来建模用户行为习惯和物品多行为分布产生的复杂混淆效应，难以有效聚合异构辅助行为，且无法在考虑偏差失真的情况下对齐跨行为表示的语义鸿沟。为解决这些局限，我们提出MCLMR——一种新颖的模型无关因果学习框架，可无缝集成到各类MBR架构中。MCLMR首先构建因果图以建模混淆效应，并通过干预实现无偏偏好估计。在该因果框架下，它采用基于混合专家机制的自适应聚合模块动态融合辅助行为信息，以及偏差感知对比学习模块以感知偏差的方式对齐跨行为表示。在三个真实数据集上的大量实验表明，MCLMR在多种基线模型上均实现了显著性能提升，验证了其有效性和通用性。所有数据和代码将公开提供。为便于匿名评审，我们的代码可通过以下链接获取：https://github.com/gitrxh/MCLMR。