http://www.bing.com:80/search?q=Microcontrollers+Board+Arduino+Raspberry+PiSearch resultshttp://www.bing.com:80/s/a/rsslogo.gifhttp://www.bing.com:80/search?q=Microcontrollers+Board+Arduino+Raspberry+PiCopyright © 2026 Microsoft. All rights reserved. These XML results may not be used, reproduced or transmitted in any manner or for any purpose other than rendering Bing results within an RSS aggregator for your personal, non-commercial use. Any other use of these results requires express written permission from Microsoft Corporation. By accessing this web page or using these results in any manner whatsoever, you agree to be bound by the foregoing restrictions.https://github.com/iLearn-Lab/MM19-MMGCNMulti-modal Graph Convolution Network is a novel multi-modal recommendation framework based on graph convolutional networks. It explicitly models modal-specific user preferences to enhance micro-video recommendation.Fri, 29 Aug 2025 02:16:00 GMThttps://dl.acm.org/doi/10.1145/3477495.3532027To tackle this problem, we propose a novel learning method named Multi-Modal Graph Contrastive Learning (MMGCL), which aims to explicitly enhance multi-modal representation learning in a self-supervised learning manner.Sat, 30 May 2026 16:33:00 GMThttp://staff.ustc.edu.cn/~hexn/papers/mm19-MMGCN.pdfWe design a Multi-modal Graph Convolution Network (MMGCN) framework built upon the message-passing idea of graph neural networks, which can yield modal-specific representations of users...Sat, 30 May 2026 22:16:00 GMThttps://zhuanlan.zhihu.com/p/668007500MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video 是 山大 && NUS && 中科大 && 合工大 人员共同发表在 MM 2019的一项工作，在短视频推荐场景中，作者引入了 GCN，提出了 多模态 GCN 模型 MMGCN。Thu, 28 May 2026 19:13:00 GMThttps://www.cnblogs.com/MTandHJ/p/17934881.htmlMMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-videoSun, 31 May 2026 23:19:00 GMThttps://www.sciencedirect.com/science/article/pii/S0950705126001474Multi-modal recommender systems, incorporating rich content information (e.g., images and texts) into user behavior modeling, have attracted significant attention recently. Current work has successfully combined graph neural networks (GNNs) and contrastive learning to improve recommendation accuracy and mitigate the inherent sparse data problem.Sun, 17 May 2026 21:34:00 GMThttps://blog.csdn.net/weiwei935707936/article/details/121643508在本文中，我们提出了利用用户项交互来指导每个模态的表示学习，并进一步个性化微视频推荐。 我们设计了一个基于图神经网络消息传递思想的多模态图卷积网络 (MMGCN)框架，它可以生成用户和微视频的特定模态表示，以更好地捕捉用户的偏好。 具体来说，我们构造了每个模态的用户项二部图，并利用每个节点的拓扑结构和邻居的特征来丰富每个节点的表示。 通过在三个公开可用的数据集Tiktok、葵和MovieLens上的广泛实验，我们证明了我们提出的模型能够显著优于目前最先进的多模式推荐方法。 总结: 为了增强微视频推荐功能，本文对特定于模态的用户偏好进行了显式建模。Fri, 15 May 2026 17:41:00 GMThttps://arxiv.org/pdf/2504.16576Section 2 (Related Work): This section presents a comprehensive review of related research, covering graph neural networks, self-supervised learning techniques, and hypergraph-based approaches in recommendation systems.Thu, 19 Mar 2026 10:41:00 GMThttps://research.monash.edu/en/publications/mmgcn-multi-modal-graph-convolution-network-for-personalized-reco/We design a Multimodal Graph Convolution Network (MMGCN) framework built upon the message-passing idea of graph neural networks, which can yield modal-specific representations of users and micro-videos to better capture user preferences.Mon, 01 Jun 2026 23:11:00 GMThttps://ilearn.qd.sdu.edu.cn/info/1028/1161.htmMMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-videoMon, 01 Jun 2026 08:38:00 GMT