Modality Alignment with Multi-scale Bilateral Attention for Multimodal
  Recommendation
Multimodal recommendation systems are increasingly becoming foundational
technologies for e-commerce and content platforms, enabling personalized
services by jointly modeling users' historical behaviors and the multimodal
features of items (e.g., visual and textual). However, most existing methods
rely on either static fusion strategies or graph-based local interaction
modeling, facing two critical limitations: (1) insufficient ability to model
fine-grained cross-modal associations, leading to suboptimal fusion quality;
and (2) a lack of global distribution-level consistency, causing
representational bias. To address these, we propose MambaRec, a novel framework
that integrates local feature alignment and global distribution regularization
via attention-guided learning. At its core, we introduce the Dilated Refinement
Attention Module (DREAM), which uses multi-scale dilated convolutions with
channel-wise and spatial attention to align fine-grained semantic patterns
between visual and textual modalities. This module captures hierarchical
relationships and context-aware associations, improving cross-modal semantic
modeling. Additionally, we apply Maximum Mean Discrepancy (MMD) and contrastive
loss functions to constrain global modality alignment, enhancing semantic
consistency. This dual regularization reduces mode-specific deviations and
boosts robustness. To improve scalability, MambaRec employs a dimensionality
reduction strategy to lower the computational cost of high-dimensional
multimodal features. Extensive experiments on real-world e-commerce datasets
show that MambaRec outperforms existing methods in fusion quality,
generalization, and efficiency. Our code has been made publicly available at
https://github.com/rkl71/MambaRec.
68c3f232651b0f6a14839d51
Kelin Ren
claimed_verified
2025-09-12T11:38:07.577Z
66f2b4d228c6e5b693a6163d
/avatars/6a9a9f474ddb51490cd671e2029c1847.svg
Kelin Ren
user
renkelin
68c3f232651b0f6a14839d52
Chan-Yang Ju
68c3f232651b0f6a14839d53
Dong-Ho Lee
Modality Alignment with Multi-scale Bilateral Attention for Multimodal
  Recommendation
Multimodal recommendation systems are increasingly becoming foundational
technologies for e-commerce and content platforms, enabling personalized
services by jointly modeling users' historical behaviors and the multimodal
features of items (e.g., visual and textual). However, most existing methods
rely on either static fusion strategies or graph-based local interaction
modeling, facing two critical limitations: (1) insufficient ability to model
fine-grained cross-modal associations, leading to suboptimal fusion quality;
and (2) a lack of global distribution-level consistency, causing
representational bias. To address these, we propose MambaRec, a novel framework
that integrates local feature alignment and global distribution regularization
via attention-guided learning. At its core, we introduce the Dilated Refinement
Attention Module (DREAM), which uses multi-scale dilated convolutions with
channel-wise and spatial attention to align fine-grained semantic patterns
between visual and textual modalities. This module captures hierarchical
relationships and context-aware associations, improving cross-modal semantic
modeling. Additionally, we apply Maximum Mean Discrepancy (MMD) and contrastive
loss functions to constrain global modality alignment, enhancing semantic
consistency. This dual regularization reduces mode-specific deviations and
boosts robustness. To improve scalability, MambaRec employs a dimensionality
reduction strategy to lower the computational cost of high-dimensional
multimodal features. Extensive experiments on real-world e-commerce datasets
show that MambaRec outperforms existing methods in fusion quality,
generalization, and efficiency. Our code has been made publicly available at
https://github.com/rkl71/MambaRec.
MambaRec enhances multimodal recommendation systems by integrating local feature alignment and global distribution regularization to improve cross-modal fusion and reduce representational bias.
multimodal recommendation systems e-commerce content platforms historical behaviors multimodal features visual textual static fusion strategies graph-based local interaction modeling fine-grained cross-modal associations global distribution-level consistency representational bias MambaRec Dilated Refinement Attention Module DREAM multi-scale dilated convolutions channel-wise attention spatial attention hierarchical relationships context-aware associations cross-modal semantic modeling Maximum Mean Discrepancy contrastive loss functions global modality alignment semantic consistency mode-specific deviations robustness dimensionality reduction scalability high-dimensional multimodal features fusion quality generalization efficiency