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arXiv · 2512.21543

CEMG: Collaborative-Enhanced Multimodal Generative Recommendation

Yuzhen Lin·Hongyi Chen·Xuanjing Chen·Shaowen Wang·Ivonne Xu·Dongming Jiang
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Abstract

Generative recommendation models often struggle with two key challenges: (1) the superficial integration of collaborative signals, and (2) the decoupled fusion of multimodal features. These limitations hinder the creation of a truly holistic item representation. To overcome this, we propose CEMG, a novel Collaborative-Enhaned Multimodal Generative Recommendation framework. Our approach features a Multimodal Fusion Layer that dynamically integrates visual and textual features under the guidance of collaborative signals. Subsequently, a Unified Modality Tokenization stage employs a Residual Quantization VAE (RQ-VAE) to convert this fused representation into discrete semantic codes. Finally, in the End-to-End Generative Recommendation stage, a large language model is fine-tuned to autoregressively generate these item codes. Extensive experiments demonstrate that CEMG significantly outperforms state-of-the-art baselines.

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Yuzhen Lin, Hongyi Chen, Xuanjing Chen, Shaowen Wang, Ivonne Xu, Dongming Jiang. 2025-12-25. CEMG: Collaborative-Enhanced Multimodal Generative Recommendation. https://arxiv.org/abs/2512.21543

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