CTR-Estimation

An index of recommendation algorithms about CTR-Estimation.

Our survey Deep Learning for Click-Through Rate Estimation is available.

Please cite our survey paper if this index is helpful.

@article{zhang2021deep,
  title={Deep learning for click-through rate estimation},
  author={Zhang, Weinan and Qin, Jiarui and Guo, Wei and Tang, Ruiming and He, Xiuqiang},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, {IJCAI} 2021, Virtual Event / Montreal, Canada, 19-27 August 2021},
  pages = {4695--4703},
  year={2021}
}

Shallow CTR Models

Name	Paper	Venue	Year	Code
LR	Richardson, M., Dominowska, E., & Ragno, R. (2007, May). Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web (pp. 521-530).	WWW	2007	NA
POLY2	Chang, Y. W., Hsieh, C. J., Chang, K. W., Ringgaard, M., & Lin, C. J. (2010). Training and testing low-degree polynomial data mappings via linear SVM. Journal of Machine Learning Research, 11(4).	JMLR	2010	NA
GBDT	He, X., Pan, J., Jin, O., Xu, T., Liu, B., Xu, T., ... & Candela, J. Q. (2014, August). Practical lessons from predicting clicks on ads at facebook. In Proceedings of the eighth international workshop on data mining for online advertising (pp. 1-9).	ADKDD	2014	NA
FM	Rendle, S. (2010, December). Factorization machines. In 2010 IEEE International conference on data mining (pp. 995-1000). IEEE.	ICDM	2010	NA
FFM	Juan, Y., Zhuang, Y., Chin, W. S., & Lin, C. J. (2016, September). Field-aware factorization machines for CTR prediction. In Proceedings of the 10th ACM conference on recommender systems (pp. 43-50).	RecSys	2016	Python
FwFM	Pan, J., Xu, J., Ruiz, A. L., Zhao, W., Pan, S., Sun, Y., & Lu, Q. (2018, April). Field-weighted factorization machines for click-through rate prediction in display advertising. In Proceedings of the 2018 World Wide Web Conference (pp. 1349-1357).	WWW	2018	Python
LorentzFM	Xu, C., & Wu, M. (2020, April). Learning feature interactions with lorentzian factorization machine. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 04, pp. 6470-6477).	AAAI	2020	Python
FM$^{2}$	Sun, Y., Pan, J., Zhang, A., & Flores, A. (2021, April). Fm2: Field-matrixed factorization machines for recommender systems. In Proceedings of the Web Conference 2021 (pp. 2828-2837).	WWW	2021	Python

Feature Interaction via DNN

Name	Paper	Venue	Year	Code
PNN	Qu, Y., Cai, H., Ren, K., Zhang, W., Yu, Y., Wen, Y., & Wang, J. (2016, December). Product-based neural networks for user response prediction. In 2016 IEEE 16th International Conference on Data Mining (ICDM) (pp. 1149-1154). IEEE.	ICDM	2016	Python
Wide&Deep	Cheng, H. T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., ... & Shah, H. (2016, September). Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems (pp. 7-10).	DLRS	2016	Python
DCN	Wang, R., Fu, B., Fu, G., & Wang, M. (2017). Deep & cross network for ad click predictions. In Proceedings of the ADKDD'17 (pp. 1-7).	ADKDD	2017	Python
FNN	Zhang, W., Du, T., & Wang, J. (2016, March). Deep learning over multi-field categorical data. In European conference on information retrieval (pp. 45-57). Springer, Cham.	ECIR	2016	Python
DeepFM	Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017, August). DeepFM: a factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 1725-1731).	ICJAI	2017	Python
NFM	He, X., & Chua, T. S. (2017, August). Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 355-364).	SIGIR	2017	Python
xDeepFM	Lian, J., Zhou, X., Zhang, F., Chen, Z., Xie, X., & Sun, G. (2018, July). xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1754-1763).	SIGKDD	2018	Python
AFM	Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., & Chua, T. S. (2017). Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv preprint arXiv:1708.04617.	ICJAI	2017	Python
FiBiNET	Huang, T., Zhang, Z., & Zhang, J. (2019, September). FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction. In Proceedings of the 13th ACM Conference on Recommender Systems (pp. 169-177).	RecSys	2019	Python
OENN	Guo, W., Tang, R., Guo, H., Han, J., Yang, W., & Zhang, Y. (2019, July). Order-aware embedding neural network for CTR prediction. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1121-1124).	SIGIR	2019
DCN V2	Wang, R., Shivanna, R., Cheng, D., Jain, S., Lin, D., Hong, L., & Chi, E. (2021, April). DCN V2: Improved deep & cross network and practical lessons for web-scale learning to rank systems. In Proceedings of the Web Conference 2021 (pp. 1785-1797).	WWW	2021	Python

Automatic Feature Interaction

Name	Paper	Venue	Year	Code
AutoInt	Song, W., Shi, C., Xiao, Z., Duan, Z., Xu, Y., Zhang, M., & Tang, J. (2019, November). Autoint: Automatic feature interaction learning via self-attentive neural networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 1161-1170).	CIKM	2019	Python
AFN	Cheng, W., Shen, Y., & Huang, L. (2020, April). Adaptive factorization network: Learning adaptive-order feature interactions. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 04, pp. 3609-3616).	AAAI	2020	Python
AutoFIS	Liu, B., Zhu, C., Li, G., Zhang, W., Lai, J., Tang, R., ... & Yu, Y. (2020, August). Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2636-2645).	SIGKDD	2020	Python
AIM	Zhu, C., Chen, B., Zhang, W., Lai, J., Tang, R., He, X., ... & Yu, Y. (2021). AIM: Automatic Interaction Machine for Click-Through Rate Prediction. IEEE Transactions on Knowledge and Data Engineering.	TKDE	2021	Python

Feature Interactions via GNN

Name	Paper	Venue	Year	Code
Fi-GNN	Li, Z., Cui, Z., Wu, S., Zhang, X., & Wang, L. (2019, November). Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 539-548).	CIKM	2019	Python
$L_{0}$-SIGN	Su, Y., Zhang, R., Erfani, S., & Xu, Z. (2021, February). Detecting beneficial feature interactions for recommender systems. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI).	AAAI	2021	Python
PCF-GNN	Li, F., Yan, B., Long, Q., Wang, P., Lin, W., Xu, J., & Zheng, B. (2021, July). Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2161-2165).	SIGIR	2021	Python
DG-ENN	Guo, W., Su, R., Tan, R., Guo, H., Zhang, Y., Liu, Z., ... & He, X. (2021, August). Dual Graph enhanced Embedding Neural Network for CTR Prediction. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 496-504).	SIGKDD	2021	NA

Sequential Feature Interactions

Name	Paper	Venue	Year	Code
DIN	Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., ... & Gai, K. (2018, July). Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1059-1068).	SIGKDD	2018	Python
DIEN	Zhou, G., Mou, N., Fan, Y., Pi, Q., Bian, W., Zhou, C., ... & Gai, K. (2019, July). Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 5941-5948).	AAAI	2019	Python
DSIN	Feng, Y., Lv, F., Shen, W., Wang, M., Sun, F., Zhu, Y., & Yang, K. (2019). Deep session interest network for click-through rate prediction. arXiv preprint arXiv:1905.06482.	ICJAI	2019	Python
DMR	Lyu, Z., Dong, Y., Huo, C., & Ren, W. (2020, April). Deep match to rank model for personalized click-through rate prediction. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 156-163).	AAAI	2020	Python
CAN	Bian, W., Wu, K., Ren, L., Pi, Q., Zhang, Y., Xiao, C., ... & Deng, H. (2022, February). CAN: Feature Co-Action Network for Click-Through Rate Prediction. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (pp. 57-65).	WSDM	2022	Python

157459387 / ctr-estimation Goto Github PK

ctr-estimation's Introduction

CTR-Estimation

Shallow CTR Models

Feature Interaction via DNN

Automatic Feature Interaction

Feature Interactions via GNN

Sequential Feature Interactions

ctr-estimation's People

Contributors

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

Recommend Org

Facebook

Microsoft

Google

Alibaba

D3

Tencent