Heterogeneous behavioral data provides comprehensive insights into user intentions and decision-making patterns. Contemporary multi-behavior recommendation models, which leverage such data to infer user preferences, typically capture high-order collaborative signals through graph neural networks on a multi-behavior heterogeneous graph or multiple behavior-specific subgraphs. However, auxiliary behaviors (e.g., view, cart) inherently contain noise that can mislead target behavior (e.g., purchase) prediction, and the incorporation of high-order collaborative signals further amplify such noise. Moreover, these approaches fail to adequately explore cross-behavior item dependencies, leading to inadequate modeling of dependencies across heterogeneous behaviors. To address these limitations, we propose Cross-behavior Item DEpendency modeling for multi-behavior Recommendation (CIDER) , a novel framework that explicitly models item dependencies across multiple types of behaviors for target behavior prediction (e.g., purchase). Specifically, our framework introduces the Hierarchical Behavior Sequence (HBS) , a data structure to systematically organize multi-behavior user–item interactions. Based on the HBS, we design a Cross-behavior Item Dependency Modeling (CIDM) module coupled with a multi-behavior cascading learning scheme to capture item-level dependencies. To enhance the robustness of the representations learned from the CIDM module, we develop an HBS-based denoising module that filters out noise inherent in auxiliary behaviors. Empirical evaluation on three benchmark datasets demonstrates the effectiveness of our model in harnessing multi-behavior data. The implementation is publicly available at https://github.com/SunJianier/CIDER .