# Triangle Graph Interest Network for Click-through Rate Prediction

arXiv: https://arxiv.org/abs/2202.02698 · HTML: https://mlsanity.com/paper/2202.02698/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 6 Feb 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)
- [Deep Session Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.06482/) (2019)
- [ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation](https://mlsanity.com/paper/1711.06632/) (2017)
- [SDM: Sequential Deep Matching Model for Online Large-scale Recommender System](https://mlsanity.com/paper/1909.00385/) (2019)
- [Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction](https://mlsanity.com/paper/2012.06968/) (2020)
- [Graph Intention Network for Click-through Rate Prediction in Sponsored Search](https://mlsanity.com/paper/2103.16164/) (2021)
- [ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation](https://mlsanity.com/paper/2005.12002/) (2020)
- [Deep Position-wise Interaction Network for CTR Prediction](https://mlsanity.com/paper/2106.05482/) (2021)
- [Looking at CTR Prediction Again: Is Attention All You Need?](https://mlsanity.com/paper/2105.05563/) (2021)
- [MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2008.05673/) (2020)

## Built on by (most cited first, 2 of 2)

- [Causality-based CTR Prediction using Graph Neural Networks](https://mlsanity.com/paper/2301.12762/) (2023)
- [On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction](https://mlsanity.com/paper/2504.08169/) (2025)

## Abstract (arXiv)

Click-through rate prediction is a critical task in online advertising. Currently, many existing methods attempt to extract user potential interests from historical click behavior sequences. However, it is difficult to handle sparse user behaviors or broaden interest exploration. Recently, some researchers incorporate the item-item co-occurrence graph as an auxiliary. Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors. Besides, those works are more biased towards popular or similar commodities. They lack an effective mechanism to break the diversity restrictions. In this paper, we point out two special properties of triangles in the item-item graphs for recommendation systems: Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we propose a novel and effective framework named Triangle Graph Interest Network (TGIN). For each clicked item in user behavior sequences, we introduce the triangles in its neighborhood of the item-item graphs as a supplement. TGIN regards these triangles as the basic units of user interests, which provide the clues to capture the real motivation for a user clicking an item. We characterize every click behavior by aggregating the information of several interest units to alleviate the elusive motivation problem. The attention mechanism determines users' preference for different interest units. By selecting diverse and relative triangles, TGIN brings in novel and serendipitous items to expand exploration opportunities of user interests. Then, we aggregate the multi-level interests of historical behavior sequences to improve CTR prediction. Extensive experiments on both public and industrial datasets clearly verify the effectiveness of our framework.