Modality Alignment with Multi-scale Bilateral Attention for Multimodal
  Recommendation
Authors: name: Kelin Ren, name: Chan-Yang Ju, name: Dong-Ho Lee

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.