Abstract
As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention from both academia and industry. Recent studies have been reported to establish CTR prediction models in the graph neural networks (GNNs) framework. However, most of GNNs-based models handle feature interactions in a complete graph, while ignoring causal relationships among features, which results in a huge drop in the performance on out-of-distribution data. This paper is dedicated to developing a causality-based CTR prediction model in the GNNs framework (Causal-GNN) integrating representations of feature graph, user graph and ad graph in the context of online advertising. In our model, a structured representation learning method (GraphFwFM) is designed to capture high-order representations on feature graph based on causal discovery among field features in gated graph neural networks (GGNNs), and GraphSAGE is employed to obtain graph representations of users and ads. Experiments conducted on three public datasets demonstrate the superiority of Causal-GNN in AUC and Logloss and the effectiveness of GraphFwFM in capturing high-order representations on causal feature graph.
Abstract (translated)
作为在线广告中普遍存在的问题,CTR预测吸引了学术界和业界的广泛关注。最近的研究表明,在Graph neural networks(GNNs)框架下建立CTR预测模型已成为研究热点。然而,大多数基于GNNs的模型在整张图处理特征相互作用,而忽视了特征之间的因果关系,这导致在离线数据上的表现急剧下降。本论文致力于在GNNs框架下开发一种基于因果关系的CTR预测模型(Causal-GNN),它将特征图、用户图和广告图的特征表示整合在一起,以在在线广告的背景下实现。在我们的模型中,一种结构化表示学习方法(GraphFwFM)被设计用于捕捉特征图上的高级别表示,基于门控图神经网络(GGNNs)中的领域特征因果关系进行建模,并使用GraphSAGE获取用户和广告的图形表示。在三个公共数据集上进行的实验表明,Causal-GNN在AUC和Logloss上的优越性和GraphFwFM在捕捉因果关系特征图上的高级别表示方面的有效性。
URL
https://arxiv.org/abs/2301.12762
