Causality-based CTR Prediction using Graph Neural Networks
This work addresses CTR prediction for online advertising by incorporating causal relationships to improve robustness, though it appears incremental as it builds on existing GNN frameworks.
The paper tackled CTR prediction in online advertising by developing a causality-based graph neural network model (Causal-GNN) that integrates feature, user, and ad graphs to address performance drops on out-of-distribution data, achieving superior AUC and Logloss on three public datasets.
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.