CaIRec：面向不完整多模态推荐的校准模态补全方法
CaIRec: Calibrated Modality Imputation for Incomplete Multimodal Recommendation
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中文总结 AI 辅助
本文针对多模态推荐中模态缺失导致的跨模态结构失真与偏好适应差距问题，提出两阶段框架CaIRec，经多组实验验证其有效性与鲁棒性。
中文摘要 AI 辅助
现实世界的多模态推荐系统常面临模态观测不完整的问题，即物品缺失图像、文本或其他内容特征，这种不完整性会削弱物品表示并降低推荐性能。现有的模态补全方法从可用物品内容估计缺失表示，但仍存在两个挑战：一是这些方法仅优化恢复的表示本身，未明确考虑其与同一物品其他模态的关系，导致补全的模态可能形成不一致的跨模态关系，引发跨模态结构失真；二是即使结构连贯的恢复信息，对个性化排序仍可能无效，恢复的表示仅获得有限的面向排序的指导，而模态缺失会破坏偏好传播所需的物品邻域，形成偏好适应差距。为应对这些挑战，本文提出面向不完整多模态推荐的校准补全方法（CaIRec），这是一个两阶段框架：结构补全校准（SIC）从可用模态推断的共享信息中估计缺失模态的表示，并通过结构正则化和观测模态对的对应监督校准其跨模态组织；面向偏好的表示校准（PRC）在表示和关系层面执行推荐特定的适配，它构建伪缺失实例以在推荐空间中通过排序监督使恢复的表示与对应观测对齐，还通过整合补全的内容关系与协同证据构建感知补全的物品图。在三个不同模态缺失设置的数据集上进行的大量实验，证明了CaIRec的有效性和鲁棒性。
英文摘要
Real-world multimodal recommender systems often face incomplete modality observations, where items lack images, text, or other content features. Such incompleteness weakens item representations and degrades recommendation performance. Existing modality imputation methods estimate missing representations from available item content, but two challenges remain. First, they optimize the recovered representation itself without explicitly considering its relations with other modalities of the same item. The completed modalities may therefore form inconsistent cross-modal relations, causing Cross-modal Structural Distortion. Second, even structurally coherent recovered information may remain ineffective for personalized ranking. Recovered representations receive limited ranking-oriented guidance, while modality missingness disrupts the item neighborhoods required for preference propagation, resulting in a Preference Adaptation Gap. To address these challenges, we propose Calibrated Imputation for Incomplete Multimodal Recommendation (CaIRec), a two-stage framework. Structural Imputation Calibration (SIC) estimates missing-modality representations from shared information inferred from available modalities and calibrates their cross-modal organization through structural regularization and correspondence supervision from observed modality pairs. Preference-oriented Representation Calibration (PRC) performs recommendation-specific adaptation at both the representation and relation levels. It constructs pseudo-missing instances to align recovered representations with observed counterparts shaped by ranking supervision in the recommendation space. It further builds completion-aware item graphs by integrating completed content relations with collaborative evidence. Extensive experiments on three datasets under different modality-missing settings demonstrate the effectiveness and robustness of CaIRec.
