A Multimodal Graph Recommendation Method Based on Cross-Attention Fusion

Kai Li, Long Xu, Cheng Zhu and Kunlun Zhang ()
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Kai Li: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410003, China
Long Xu: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410003, China
Cheng Zhu: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410003, China
Kunlun Zhang: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410003, China

Mathematics, 2024, vol. 12, issue 15, 1-16

Abstract: Research on recommendation methods using multimodal graph information presents a significant challenge within the realm of information services. Prior studies in this area have lacked precision in the purification and denoising of multimodal information and have insufficiently explored fusion methods. We introduce a multimodal graph recommendation approach leveraging cross-attention fusion. This model enhances and purifies multimodal information by embedding the IDs of items and their corresponding interactive users, thereby optimizing the utilization of such information. To facilitate better integration, we propose a cross-attention mechanism-based multimodal information fusion method, which effectively processes and merges related and differential information across modalities. Experimental results on three public datasets indicated that our model performed exceptionally well, demonstrating its efficacy in leveraging multimodal information.

Keywords: multimodal graph; recommendation method; multimodal information purification; cross-attention mechanism; information fusion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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