The goal of recommendation is to provide users with personalized item suggestions. However, data-driven recommendations suffer from popularity bias caused by imbalanced user–item interactions; this problem becomes more severe in multi-behavior settings because popularity bias is both complex across behaviors and transmissible along behavior cascades, making it infeasible to simply apply single-behavior debiasing methods directly. To this end, we propose a novel popularity debiasing framework for multi-behavior recommendation via Causal Inference and Data Augmentation (CIDA), which consists of a Popularity-aware Noise Weighting (PNW) module and a Simulated Unpopular item Augmentation (SUA) module. To cope with the complexity of popularity bias, PNW adjusts item representations by applying controllable noise weights based on item popularity. Meanwhile, SUA introduces simulated nodes into the causal graph to block the direct causal effect between items and recommendation outcomes. Inspired by imitation learning, we design a minimum distance constraint in feature space between simulated and real unpopular items to maintain representational similarity. A feature fusion strategy is then used to generate unbiased item representations. To integrate the two modules, we design a noise augmentation contrastive learning task, enabling the unbiased item features to propagate effectively across multi-behavior chain and suppress popularity bias transmission. Extensive experiments on three real-world datasets validate the effectiveness of our framework and the rationality of its design.
