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Awesome-Click-Through-Rate-Prediction

★ 19 Open GitHub ↗

A curated list of papers on click-through-rate (CTR) prediction.

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# Awesome-Click-Through-Rate-Prediction
![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-yellow.svg)](https://github.com/YuanchenBei/Awesome-Click-Through-Rate-Prediction) 
![Stars](https://img.shields.io/github/stars/YuanchenBei/Awesome-Click-Through-Rate-Prediction?color=green)

This repository contains a curated list of papers related to **click-through-rate (CTR) prediction/online recommendations**, which are categorized based on their technical categories and published years.

*Continuously updating!*

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### Feature Interaction
(WWW 2021) **FM2: Field-matrixed Factorization Machines for Recommender Systems** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3442381.3449930) [[Code]](https://github.com/yahoo/FmFM)

(IJCAI 2021) **A Dual Input-aware Factorization Machine for CTR Prediction** [[Paper]](https://www.ijcai.org/Proceedings/2020/0434.pdf)

(AAAI 2019) **Holographic Factorization Machines for Recommendation** [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/download/4448/4326)

(CIKM 2019) **Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3357384.3357951) [[Code]](https://github.com/JiangShaoYin/FIGNN)

(RecSys 2019) **FiBiNET: Combining Feature Importance and Bilinear Feature Interaction for Click-Through Rate Prediction** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3298689.3347043)

(WWW 2018) **Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3178876.3186040)

(SIGIR 2017) **Neural Factorization Machines for Sparse Predictive Analytics** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777) [[Code]](https://github.com/hexiangnan/neural_factorization_machine)

(IJCAI 2017) **Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks**  [[Paper]](https://www.ijcai.org/proceedings/2017/0435.pdf) [[Code]](https://github.com/hexiangnan/attentional_factorization_machine)

(IJCAI 2017) **DeepFM: A Factorization-Machine based Neural Network for CTR Prediction** [[Paper]](https://www.ijcai.org/proceedings/2017/0239.pdf)

(DLRS 2016) **Wide & Deep Learning for Recommender Systems** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)

(ICDM 2016) **Product-based Neural Networks for User Response Prediction** [[Paper]](https://arxiv.org/pdf/1611.00144.pdf) [[Code]](https://github.com/Atomu2014/product-nets)

(RecSys 2016) **Field-aware Factorization Machines for CTR Prediction** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134)

(ICDM 2010) **Factorization Machines** [[Paper]](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf)

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### Interest Modeling

(WWW 2024) **Macro Graph Neural Networks for Online Billion-Scale Recommender Systems** [[Paper]](https://arxiv.org/pdf/2401.14939.pdf) [[Code]](https://github.com/YuanchenBei/MacGNN)

(CIKM 2023) **Non-Recursive Cluster-Scale Graph Interacted Model for Click-Through Rate Prediction** [[Paper]](https://dl.acm.org/doi/10.1145/3583780.3615180) [[Code]](https://github.com/YuanchenBei/NRCGI)

(SIGIR 2022) **Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer** [[Paper]](https://arxiv.org/pdf/2201.13311.pdf) [[Code]](https://github.com/qwerfdsaplking/F2R-HMT)

(SIGIR 2022) **Deep Multi-Representational Item Network for CTR Prediction** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3477495.3531845) [[Code]](https://github.com/alibaba/deep-multi-representational-item-network)

(CIKM 2022) **Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction** [[Paper]](https://arxiv.org/pdf/2306.10028.pdf)

(CIKM 2020) **Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction** [[Paper]](https://arxiv.org/pdf/2006.05639.pdf)

(KDD 2019) **Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3292500.3330666) [[Code]](https://github.com/UIC-Paper/MIMN)

(SIGIR 2019) **Graph Intention Network for Click-through Rate Prediction in Sponsored Search** [[Paper]](https://dl.acm.org/doi/10.1145/3331184.3331283)

(IJCAI 2019) **Deep Session Interest Network for Click-Through Rate Prediction** [[Paper]](https://arxiv.org/pdf/1905.06482.pdf) [[Code]](https://github.com/shenweichen/DSIN)

(AAAI 2019) **Deep Interest Evolution Network for Click-Through Rate Prediction** [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/download/4545/4423) [[Code]](https://github.com/mouna99/dien)

(KDD 2018) **Deep Interest Network for Click-Through Rate Prediction** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)

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### Related Survey
(IPM 2022) **Click-through Rate Prediction in Online Advertising: A Literature Review** [[Paper]](https://arxiv.org/pdf/2202.10462.pdf)

(IJCAI 2019) **Deep Learning for Click-Through Rate Estimation** [[Paper]](https://www.ijcai.org/proceedings/2021/0636.pdf)

Awesome-Click-Through-Rate-Prediction

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