知识感知的扩散增强多媒体推荐
Knowledge-aware Diffusion-Enhanced Multimedia Recommendation
AI总结：
本文提出知识感知扩散增强架构KDiffE，通过注意力感知矩阵和引导扩散生成低噪声任务相关知识图谱，结合对比学习提升多媒体推荐性能。
摘要：
多媒体推荐旨在利用丰富的多媒体内容来增强历史用户-物品交互信息，这不仅可以指示物品之间的内容相关性，还可以揭示用户更细粒度的偏好。本文提出了一种基于对比学习范式的知识感知扩散增强架构（KDiffE），用于多媒体推荐。具体而言，我们首先利用原始用户-物品图在图神经网络中构建注意力感知矩阵，该矩阵可以学习用户与物品之间的重要性以用于主视图构建。注意力感知矩阵通过采用带重启的随机游走策略构建，能够保留用户与物品之间的重要性，从而生成注意力感知节点特征的聚合。然后，我们提出了一种引导扩散模型，用于生成噪声更少且与任务强相关的知识图谱，以构建知识感知对比视图，该模型利用与物品相连的用户嵌入来引导与任务强相关的知识图谱生成，从而增强物品的语义信息。我们在三个多媒体数据集上进行了全面实验，结果表明我们的KDiffE及其组件在各种最先进方法上的有效性。我们的源代码可在 https://github.com/1453216158/KDiffE 获取。
英文摘要：
Multimedia recommendations aim to use rich multimedia content to enhance historical user-item interaction information, which can not only indicate the content relatedness among items but also reveal finer-grained preferences of users. In this paper, we propose a Knowledge-aware Diffusion-Enhanced architecture using contrastive learning paradigms (KDiffE) for multimedia recommendations. Specifically, we first utilize original user-item graphs to build an attention-aware matrix into graph neural networks, which can learn the importance between users and items for main view construction. The attention-aware matrix is constructed by adopting a random walk with a restart strategy, which can preserve the importance between users and items to generate aggregation of attention-aware node features. Then, we propose a guided diffusion model to generate strongly task-relevant knowledge graphs with less noise for constructing a knowledge-aware contrastive view, which utilizes user embeddings with an edge connected to an item to guide the generation of strongly task-relevant knowledge graphs for enhancing the item's semantic information. We perform comprehensive experiments on three multimedia datasets that reveal the effectiveness of our KDiffE and its components on various state-of-the-art methods. Our source codes are available https://github.com/1453216158/KDiffE.
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。
