Recently, multimodal recommendations have gained increasing attention for effectively addressing the data sparsity problem by incorporating modality-based representations. Although multimodal recommendations excel in accuracy, the introduction of different modalities (e.g., images, text, and audio) may expose more users' sensitive information (e.g., gender and age) to recommender systems, resulting in potentially more serious unfairness issues. Despite many efforts on fairness, existing fairness-aware methods are either incompatible with multimodal scenarios, or lead to suboptimal fairness performance due to neglecting sensitive information of multimodal content. To achieve counterfactual fairness in multimodal recommendations, we propose a novel fairness-aware multimodal recommendation approach (dubbed as FMMRec) to disentangle the sensitive and non-sensitive information from modal representations and leverage the disentangled modal representations to guide fairer representation learning. Specifically, we first disentangle biased and filtered modal representations by maximizing and minimizing their sensitive attribute prediction ability respectively. With the disentangled modal representations, we mine the modality-based unfair and fair (corresponding to biased and filtered) user-user structures for enhancing explicit user representation with the biased and filtered neighbors from the corresponding structures, followed by adversarially filtering out sensitive information. Experiments on two real-world public datasets demonstrate the superiority of our FMMRec relative to the state-of-the-art baselines. Our source code is available at https://anonymous.4open.science/r/FMMRec.

翻译：近年来，多模态推荐通过引入模态表征有效解决了数据稀疏性问题而受到广泛关注。尽管多模态推荐在准确性方面表现出色，但不同模态（如图像、文本和音频）的引入可能向推荐系统暴露更多用户的敏感信息（如性别和年龄），从而导致更严重的公平性问题。尽管已有诸多针对公平性的研究，但现有公平感知方法要么与多模态场景不兼容，要么因忽略多模态内容中的敏感信息而导致公平性能次优。为实现多模态推荐中的反事实公平性，我们提出了一种新颖的公平感知多模态推荐方法（称为FMMRec），用于从模态表征中分离敏感与非敏感信息，并利用解耦后的模态表征指导更公平的表征学习。具体而言，我们首先通过分别最大化与最小化偏置模态表征和过滤模态表征的敏感属性预测能力来实现解耦。基于解耦后的模态表征，我们挖掘基于模态的不公平与公平（分别对应于偏置与过滤）用户-用户结构，通过从对应结构中聚合偏置与过滤邻居来增强显式用户表征，随后以对抗方式过滤敏感信息。在两个真实世界公共数据集上的实验表明，我们的FMMRec相较于最先进基线方法具有优越性。我们的源代码可在https://anonymous.4open.science/r/FMMRec获取。