Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer

Abstract

Click-Through Rate (CTR) prediction, is an essential component of online advertising. The mainstream techniques mostly focus on feature interaction or user interest modeling, which rely on users' directly interacted items. The performance of these methods is usually impeded by inactive behaviours and system's exposure, incurring that the features extracted do not contain enough information to represent all potential interests. For this sake, we propose Neighbor-Interaction based CTR prediction, which put this task into a Heterogeneous Information Network (HIN) setting, then involves local neighborhood of the target user-item pair in the HIN to predict their linkage. In order to enhance the representation of the local neighbourhood, we consider four types of topological interaction among the nodes, and propose a novel Graph-masked Transformer architecture to effectively incorporates both feature and topological information. We conduct comprehensive experiments on two real world datasets and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly. CCS CONCEPTS• Information systems → Computational advertising.

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

“…Recent few years have seen deep graph learning (DGL) based on graph neural networks (GNNs) making remarkable progress in a variety of important areas, ranging from business scenarios such as finance (e.g., fraud detection and credit modeling) [28,144,9,67], e-commerce (e.g., recommendation system) [126,85], drug discovery and advanced material discovery [41,134,100,82,83]. Despite the progress, applying various DGL algorithms to real-world applications faces a Inherent Noise D train = (A + a , X + x , Y + y ) [164], [80], [87], [93], [72], [24] [101], [115] Distribution shift P train (G, Y ) = P test (G, Y )…”

“…In the past few years, DGL is becoming an active frontier of deep learning with an exponential growth of research. With advantages in modeling graph-structured data, DGL has achieved remarkable progress in many important areas, ranging from finance (e.g., fraud detection and credit modeling) [28,144,9,67], e-commerce (e.g., recommendation system) [126,85], drug discovery and advanced material discovery [41,134,100,82,83].…”