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This paper introduces CaIRec, a two-stage framework designed to address the challenges of incomplete modality observations in multimodal recommender systems. By employing Structural Imputation Calibration (SIC) to ensure coherent cross-modal relations and Preference-oriented Representation Calibration (PRC) to enhance ranking effectiveness, CaIRec significantly improves item representation and recommendation performance. Experimental results across three datasets reveal that CaIRec outperforms existing modality imputation methods, effectively bridging the gaps caused by missing modalities.
Incomplete modalities can lead to significant distortions in multimodal recommendations, but CaIRec effectively calibrates item representations to enhance performance and coherence.
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.
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