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

arXiv: https://arxiv.org/abs/2103.16164 · HTML: https://mlsanity.com/paper/2103.16164/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)

## Built on by (most cited first, 14 of 14)

- [Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer](https://mlsanity.com/paper/2108.11513/) (2021)
- [Causality-based CTR Prediction using Graph Neural Networks](https://mlsanity.com/paper/2301.12762/) (2023)
- [Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation](https://mlsanity.com/paper/2208.01849/) (2022)
- [Preference Discerning with LLM-Enhanced Generative Retrieval](https://mlsanity.com/paper/2412.08604/) (2024)
- [Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification](https://mlsanity.com/paper/2106.06873/) (2021)
- [Triangle Graph Interest Network for Click-through Rate Prediction](https://mlsanity.com/paper/2202.02698/) (2022)
- [IA-GCN: Interactive Graph Convolutional Network for Recommendation](https://mlsanity.com/paper/2204.03827/) (2022)
- [Spatial Autoregressive Coding for Graph Neural Recommendation](https://mlsanity.com/paper/2205.09489/) (2022)
- [ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users](https://mlsanity.com/paper/2510.09393/) (2025)
- [A Deep Behavior Path Matching Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2302.00302/) (2023)
- [Graph Entropy Minimization for Semi-supervised Node Classification](https://mlsanity.com/paper/2305.19502/) (2023)
- [Light-weight End-to-End Graph Interest Network for CTR Prediction in E-commerce Search](https://mlsanity.com/paper/2406.17745/) (2024)
- [Wireless Power Transfer and Intent-Driven Network Optimization in AAVs-assisted IoT for 6G Sustainable Connectivity](https://mlsanity.com/paper/2511.18368/) (2025)
- [E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems](https://mlsanity.com/paper/2511.20564/) (2025)

## Abstract (arXiv)

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.