Graph Intention Network for Click-through Rate Prediction in Sponsored Search

TL;DR

This paper introduces Graph Intention Network (GIN), a novel graph-based model that enhances click-through rate prediction by effectively capturing user intentions and addressing behavior sparsity and weak generalization issues.

Contribution

GIN is the first to integrate graph learning with CTR prediction, jointly training for improved user intention mining and CTR estimation in sponsored search.

Findings

Outperforms existing deep learning models offline.

Achieves significant CTR improvements online.

Enriches user behavior data through multi-layered graph diffusion.

Abstract

Estimating click-through rate (CTR) accurately has an essential impact on improving user experience and revenue in sponsored search. For CTR prediction model, it is necessary to make out user real-time search intention. Most of the current work is to mine their intentions based on user real-time behaviors. However, it is difficult to capture the intention when user behaviors are sparse, causing the behavior sparsity problem. Moreover, it is difficult for user to jump out of their specific historical behaviors for possible interest exploration, namely weak generalization problem. We propose a new approach Graph Intention Network (GIN) based on co-occurrence commodity graph to mine user intention. By adopting multi-layered graph diffusion, GIN enriches user behaviors to solve the behavior sparsity problem. By introducing co-occurrence relationship of commodities to explore the potential preferences, the weak generalization problem is also alleviated. To the best of our knowledge, the GIN method is the first to introduce graph learning for user intention mining in CTR prediction and propose end-to-end joint training of graph learning and CTR prediction tasks in sponsored search. At present, GIN has achieved excellent offline results on the real-world data of the e-commerce platform outperforming existing deep learning models, and has been running stable tests online and achieved significant CTR improvements.