Abstract
Multimodal recommender systems enhance personalized recommendations in e-commerce and online advertising by integrating visual, textual, and user-item interaction data. However, existing methods often overlook two critical biases: (i) modal confounding, where latent factors (e.g., brand style or product category) simultaneously drive multiple modalities and influence user preference, leading to spurious feature-preference associations; (ii) interaction bias, where genuine user preferences are mixed with noise from exposure effects and accidental clicks. To address these challenges, we propose a Causal-inspired multimodal Recommendation framework. Specifically, we introduce a dual-channel cross-modal diffusion module to identify hidden modal confounders, utilize back-door adjustment with hierarchical matching and vector-quantized codebooks to block confounding paths, and apply front-door adjustment combined with causal topology reconstruction to build a deconfounded causal subgraph. Extensive experiments on three real-world e-commerce datasets demonstrate that our method significantly outperforms state-of-the-art baselines while maintaining strong interpretability.
Abstract (translated)
多模态推荐系统通过融合视觉、文本和用户-物品交互数据，提升了电子商务和在线广告中的个性化推荐效果。然而，现有方法往往忽略了两个关键偏差：（i）模式混淆，即潜在因素（例如品牌风格或产品类别）同时驱动多个模式，并影响用户的偏好，导致虚假的特征-偏好关联；（ii）交互偏倚，即真正的用户偏好与曝光效应和意外点击产生的噪声混合在一起。为解决这些问题，我们提出了一个基于因果关系的多模态推荐框架。具体来说，我们引入了一个双通道跨模式扩散模块来识别隐藏的模式混淆因素，利用分层匹配和向量量化代码本进行背门调整以阻断混淆路径，并采用前门调整结合因果拓扑重构建立去混杂的因果子图。在三个现实世界的电子商务数据集上进行了广泛的实验，证明了我们的方法显著优于最先进的基准模型，同时保持了强大的可解释性。
URL
https://arxiv.org/abs/2510.12325
