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Keywords = multimodal information purification

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16 pages, 714 KiB  
Article
A Multimodal Graph Recommendation Method Based on Cross-Attention Fusion
by Kai Li, Long Xu, Cheng Zhu and Kunlun Zhang
Mathematics 2024, 12(15), 2353; https://doi.org/10.3390/math12152353 - 28 Jul 2024
Cited by 3 | Viewed by 2891
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 [...] Read more.
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. Full article
(This article belongs to the Section E1: Mathematics and Computer Science)
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