Multi-behavior recommendation faces a critical challenge in practice: auxiliary behaviors (e.g., clicks, carts) are often noisy, weakly correlated, or semantically misaligned with the target behavior (e.g., purchase), which leads to biased preference learning and suboptimal performance. While existing methods attempt to fuse these heterogeneous signals, they inherently lack a principled mechanism to ensure robustness against such behavioral inconsistency. In this work, we propose Robust Multi-Behavior Recommendation towards Target Behaviors (RMBRec), a robust multi-behavior recommendation framework grounded in an information-theoretic robustness principle. We interpret robustness as a joint process of maximizing predictive information while minimizing its variance across heterogeneous behavioral environments. Under this perspective, the Representation Robustness Module (RRM) enhances local semantic consistency by maximizing the mutual information between users' auxiliary and target representations, whereas the Optimization Robustness Module (ORM) enforces global stability by minimizing the variance of predictive risks across behaviors, which is an efficient approximation to invariant risk minimization. This local-global collaboration bridges representation purification and optimization invariance in a theoretically coherent way. Extensive experiments on three real-world datasets demonstrate that RMBRec not only outperforms state-of-the-art methods in accuracy but also maintains remarkable stability under various noise perturbations. For reproducibility, our code is available at https://github.com/miaomiao-cai2/RMBRec/.

翻译：多行为推荐在实践中面临一个关键挑战：辅助行为（如点击、加入购物车）通常存在噪声、与目标行为（如购买）弱相关或语义不对齐，这会导致偏好学习出现偏差和性能次优。尽管现有方法尝试融合这些异构信号，但它们本质上缺乏一种原则性机制来确保对此类行为不一致性的鲁棒性。在本工作中，我们提出了面向目标行为的鲁棒多行为推荐框架（RMBRec），这是一个基于信息论鲁棒性原理的鲁棒多行为推荐框架。我们将鲁棒性解释为一个联合过程：在最大化预测信息的同时，最小化其在异构行为环境中的方差。基于这一视角，表示鲁棒性模块（RRM）通过最大化用户辅助行为表示与目标行为表示之间的互信息来增强局部语义一致性；而优化鲁棒性模块（ORM）则通过最小化跨行为预测风险的方差来强制全局稳定性，这是对不变风险最小化的一种高效近似。这种局部-全局协作以理论一致的方式桥接了表示纯化与优化不变性。在三个真实世界数据集上的大量实验表明，RMBRec不仅在准确性上优于现有最先进方法，而且在各种噪声扰动下保持了显著的稳定性。为促进可复现性，我们的代码公开于 https://github.com/miaomiao-cai2/RMBRec/。