arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新，休息一下吧，周末愉快~~
arXiv 2512.19983cs.IR
收藏

IGDMRec: 基于行为的物品图扩散用于多模态推荐

IGDMRec: Behavior Conditioned Item Graph Diffusion for Multimodal Recommendation

School of Information Technology, Carleton University（卡尔顿大学信息技术学院）

Ziyuan Guo, Jie Guo, Zhenghao Chen, Bin Song, Fei Richard Yu

备注：
12 pages, 6 figures. This paper has been accepted for publication in IEEE Transactions on Multimedia. The final published version will be available via IEEE Xplore
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

AI总结：

IGDMRec通过行为条件图扩散和条件去噪网络，利用用户行为信息去噪多模态推荐的语义物品图，提升推荐性能。

摘要：

多模态推荐系统（MRSs）对于各种在线平台至关重要，通过整合物品的多模态信息为用户提供更准确的个性化推荐。基于结构的MRSs通过构建语义物品图实现了最先进的性能，该图基于模态特征相似性显式建模物品之间的关系。然而，此类语义物品图往往因1）多模态信息中的固有噪声和2）物品语义与用户-物品共现关系之间的不匹配而变得嘈杂，这会引入虚假链接并导致次优推荐。为了解决这一挑战，我们提出了多模态推荐的物品图扩散（IGDMRec），一种利用扩散模型与分类器自由引导来去噪语义物品图的新方法，通过整合用户行为信息。具体而言，IGDMRec引入了行为条件图扩散（BGD）模块，将交互数据作为条件信息来引导语义物品图的去噪。此外，设计了一个条件去噪网络（CD-Net）以实现去噪过程并保持可控的复杂度。最后，我们提出了一种对比表示增强方案，利用去噪的物品图和原始物品图来增强物品表示。大量实验在四个真实世界数据集上展示了IGDMRec在竞争基线上的优越性，鲁棒性分析验证了其去噪能力，消融研究验证了其关键组件的有效性。

英文摘要：

Multimodal recommender systems (MRSs) are critical for various online platforms, offering users more accurate personalized recommendations by incorporating multimodal information of items. Structure-based MRSs have achieved state-of-the-art performance by constructing semantic item graphs, which explicitly model relationships between items based on modality feature similarity. However, such semantic item graphs are often noisy due to 1) inherent noise in multimodal information and 2) misalignment between item semantics and user-item co-occurrence relationships, which introduces false links and leads to suboptimal recommendations. To address this challenge, we propose Item Graph Diffusion for Multimodal Recommendation (IGDMRec), a novel method that leverages a diffusion model with classifier-free guidance to denoise the semantic item graph by integrating user behavioral information. Specifically, IGDMRec introduces a Behavior-conditioned Graph Diffusion (BGD) module, incorporating interaction data as conditioning information to guide the denoising of the semantic item graph. Additionally, a Conditional Denoising Network (CD-Net) is designed to implement the denoising process with manageable complexity. Finally, we propose a contrastive representation augmentation scheme that leverages both the denoised item graph and the original item graph to enhance item representations. \LL{Extensive experiments on four real-world datasets demonstrate the superiority of IGDMRec over competitive baselines, with robustness analysis validating its denoising capability and ablation studies verifying the effectiveness of its key components.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑