Beyond Superficial Cues: Mitigating Popularity-Amplified Presentation Bias in Multimodal Recommendation

Multimodal recommendation systems significantly enhance user experience by leveraging rich multimodal features. However, their effectiveness is often undermined by presentation bias, a prevalent issue where user interactions are disproportionately influenced by superficial presentation cues (e.g., flashy thumbnails, sensational titles) rather than intrinsic content. Such cues are often highly correlated with item popularity, leading to a popularity-amplified presentation bias that compromises recommendation fairness. To address this fundamental challenge, this paper introduces a novel Intrinsic **CO**ntent **RE**presentation (**CORE**) framework. We formalize presentation bias within a Structural Causal Model (SCM), revealing that conditioning on fully coupled multimodal features acts as a collider, inducing spurious correlations between intrinsic content and presentation manner. CORE mitigates this collider bias by disentangling an item's multimodal representation into two independent components: a content-aware representation and a bias-aware representation. This is achieved through a carefully designed automated contrastive learning scheme and enforced by an independence constraint. By performing recommendations based solely on the purified intrinsic content representation, our model effectively performs a causal intervention to mitigate the spurious effects. Extensive experiments on three real-world multimodal datasets demonstrate that CORE achieves significant and consistent improvements in both recommendation accuracy and fairness.