Recently, multimodal recommendations (MMR) have gained increasing attention for alleviating the data sparsity problem of traditional recommender systems by incorporating modality-based representations. Although MMR exhibit notable improvement in recommendation accuracy, we empirically validate that an increase in the quantity or variety of modalities leads to a higher degree of users' sensitive information leakage due to entangled causal relationships, risking fair representation learning. On the other hand, existing fair representation learning approaches are mostly based on the assumption that sensitive information is solely leaked from users' interaction data and do not explicitly model the causal relationships introduced by multimodal data, which limits their applicability in multimodal scenarios. Particularly, we disentangle biased and filtered modal embeddings inspired by causal inference techniques, enabling the mining of modality-based unfair and fair user-user relations, thereby enhancing the fairness and informativeness of user representations. By addressing the causal effects of sensitive attributes on user preferences, our approach aims to achieve counterfactual fairness in multimodal recommendations. Experiments on two public datasets demonstrate the superiority of our FMMRec relative to the state-of-the-art baselines. Our source code is available at https://github.com/WeixinChen98/FMMRec.

翻译：近年来，多模态推荐通过融合基于模态的表征，在缓解传统推荐系统数据稀疏性问题方面日益受到关注。尽管多模态推荐在推荐准确性上表现出显著提升，我们通过实证验证发现，由于纠缠的因果关系，模态数量或种类的增加会导致用户敏感信息泄露程度的加剧，从而危及公平表征学习。另一方面，现有的公平表征学习方法大多基于敏感信息仅从用户交互数据中泄露的假设，且未显式建模由多模态数据引入的因果关系，这限制了其在多模态场景下的适用性。具体而言，我们受因果推断技术启发，解耦了有偏和过滤后的模态嵌入，从而能够挖掘基于模态的不公平与公平的用户-用户关系，进而提升用户表征的公平性与信息量。通过处理敏感属性对用户偏好的因果效应，我们的方法旨在实现多模态推荐的反事实公平性。在两个公开数据集上的实验证明了我们的FMMRec相对于最先进基线的优越性。我们的源代码可在https://github.com/WeixinChen98/FMMRec获取。