论文标题
迈向更好的图形表示：用于多模式营销意图检测的两分支协作图神经网络
Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection
论文作者
论文摘要
受到以下事实的启发：通过互联网传播和收集信息成为常态，越来越多的人选择在社交网络中发布营利性内容（图像和文本）。由于网络审查员的困难，恶意营销可能会损害社会。因此，自动在线检测营销意图是有意义的。但是，多模式数据之间的差距使得很难融合图像和文本以进行内容营销检测。为此，本文提出了两个分支协作图神经网络，以端到端的方式通过图形卷积网络（GCN）代表多模式数据。实验结果表明，我们提出的方法可实现营销意图检测的出色图形分类性能。
Inspired by the fact that spreading and collecting information through the Internet becomes the norm, more and more people choose to post for-profit contents (images and texts) in social networks. Due to the difficulty of network censors, malicious marketing may be capable of harming society. Therefore, it is meaningful to detect marketing intentions online automatically. However, gaps between multimodal data make it difficult to fuse images and texts for content marketing detection. To this end, this paper proposes Two-Branch Collaborative Graph Neural Networks to collaboratively represent multimodal data by Graph Convolution Networks (GCNs) in an end-to-end fashion. Experimental results demonstrate that our proposed method achieves superior graph classification performance for marketing intention detection.
