SG-UMP: Sequence-Guided Universal Multimodal Prioritization Calculation Framework

• URL: http://arxiv.org/abs/2608.28503v1
• Date: Fri, 28 Aug 2026 16:34:27 GMT
• Title: SG-UMP: Sequence-Guided Universal Multimodal Prioritization Calculation Framework
• Abstract summary: Multimodal recommendation (MSR) improves by incorporating heterogeneous information such as text, images, and user interactions.<n>Existing MSR methods fail to capture user-level preference heterogeneity and dataset-level modality bias.<n>We propose textbfSG-UMP, a plug-and-play plugin for enhancing multimodal information processing in MSR.
• Score: 12.105489448666267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal sequential recommendation (MSR) improves recommendation by incorporating heterogeneous information such as text, images, and user interactions. However, existing MSR methods often fail to capture user-level preference heterogeneity and dataset-level modality bias, limiting their adaptability across users and datasets. To address this issue, we propose \textbf{S}equence-\textbf{G}uided \textbf{U}niversal \textbf{M}ultimodal \textbf{P}rioritization Calculation Framework (\textbf{SG-UMP}), a plug-and-play plugin for enhancing multimodal information processing in MSR. SG-UMP includes a Module Combiner for flexible multimodal processing and a Module Router for dynamic module ordering, enabling adaptation to both user preferences and dataset characteristics. Experiments on four real-world datasets show that SG-UMP consistently improves recommendation performance across different backbones and multimodal settings. The code is available at https://github.com/esemsc-xz524/SG-UMP .

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