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

TL;DR

This paper introduces a novel graph-masked transformer model that leverages local neighborhood interactions in a heterogeneous information network to improve click-through rate prediction, outperforming existing methods and demonstrating significant online gains.

Contribution

The paper proposes a new neighbor-interaction based CTR prediction framework using a graph-masked transformer to incorporate diverse neighborhood interactions for better user interest modeling.

Findings

Outperforms state-of-the-art CTR models on real datasets

Achieves a 21.9% CTR increase in online A/B tests

Proves effectiveness of neighborhood interaction modeling

Abstract

Click-Through Rate (CTR) prediction, which aims to estimate the probability that a user will click an item, is an essential component of online advertising. Existing methods mainly attempt to mine user interests from users' historical behaviours, which contain users' directly interacted items. Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests. To tackle these problems, we propose Neighbor-Interaction based CTR prediction (NI-CTR), which considers this task under a Heterogeneous Information Network (HIN) setting. In short, Neighbor-Interaction based CTR prediction involves the local neighborhood of the target user-item pair in the HIN to predict their linkage. In order to guide the representation learning of the local neighbourhood, we further consider different kinds of interactions among the local neighborhood nodes from both explicit and implicit perspective, and propose a novel Graph-Masked Transformer (GMT) to effectively incorporates these kinds of interactions to produce highly representative embeddings for the target user-item pair. Moreover, in order to improve model robustness against neighbour sampling, we enforce a consistency regularization loss over the neighbourhood embedding. We conduct extensive experiments on two real-world datasets with millions of instances and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly. Meanwhile, the comprehensive ablation studies verify the effectiveness of every component of our model. Furthermore, we have deployed this framework on the WeChat Official Account Platform with billions of users. The online A/B tests demonstrate an average CTR improvement of 21.9 against all online baselines.