CTR Prediction#

A curated list of CTR prediction models

Feature Interaction#

2023

FinalNet [1]
SIGIR’23
Huawei

FinalMLP [2]
AAAI’23
Huawei

EulerNet [3]
SIGIR’23
Huawei

GDCN [4]
CIKM’23
Microsoft

MemoNet [5]
CIKM’23
Sina Weibo

AdaEnsemble [6]
AdKDD’23
Credit Karma

2022

FRNet [7]
SIGIR’22
Microsoft

APG [8]
NeurIPS’22
Alibaba

FINT [9]
ICASSP’22
iQIYI

DHEN [10]
DLP-KDD’22
Meta

2021

DCN-V2 [11]
WWW’21
Google

FM2 [12]
WWW’21
Yahoo

EDCN [13]
CIKM’21
Huawei

DESTINE [14]
CIKM’21
Alibaba

SAM [15]
SIGIR’21
BOSS Zhipin

PCF-GNN [16]
SIGIR’21
Alibaba

xLightFM [17]
SIGIR’21

AOANet [18]
KDD’21
Didi Chuxing

DCAP [19]
CIKM’21

xDeepInt [20]
DLP-KDD’21
Credit Karma

2020

AFN [21]
AAAI’20

DeepIM [22]
CIKM’20
Alibaba

AutoGroup [23]
SIGIR’20
Huawei

FWL [24]
NeurIPS’20

ONN [25]
NeuralNets’20

DIFM [26]
IJCAI’20

AutoFIS [27]
KDD’20
Huawei

AutoCTR [28]
KDD’20
Facebook

GLIDER [29]
ICLR’20
Facebook

2019

AutoInt [30]
CIKM’19

FiGNN [31]
CIKM’19

FGCNN [32]
WWW’19
Huawei

FiBiNET [33]
RecSys’19
Sina Weibo

HFM [34]
AAAI’19

DLRM [35]
Arxiv’19
Facebook

IFM [36]
IJCAI’19

2018

FwFM [37]
WWW’18
Yahoo

xDeepFM [38]
KDD’18
Microsoft

2017

NFM [39]
SIGIR’17

FFM [40]
WWW’17
Criteo

DCN [41]
ADKDD’17
Google

DeepFM [42]
IJCAI’17
Huawei

AFM [43]
IJCAI’17

2016
&before

FFM [44]
RecSys’16

YoutubeDNN [1]
RecSys’16
Google

PNN [46]
ICDM’16

Wide&Deep [47]
DLRS’16
Google

DeepCrossing [48]
KDD’16
Microsoft

HOFM [49]
NIPS’16

DeepCTR [50]
MM’16

CCPM [51]
CIKM’15

LR+GBDT [52]
ADKDD’14
Facebook

FTRL [53]
KDD’13
Google

FM [54]
ICDM’10

LR [55]
WWW’07
Microsoft

Behaviour Sequence Modeling#

2023

TWIN [56]
KDD’23
Kuaishou

DCIN [57]
CIKM’23
Meituan

2022

SDIM [58]
CIKM’22
Meituan

DINMP [59]
SDM’22
Alibaba

2021

CIN [60]
TKDD’21

HyperCTR [61]
CIKM’21

2020

DMIN [62]
CIKM’20
Alibaba

MARN [63]
WWW’20
Alibaba

2019

DIEN [64]
AAAI’19
Alibaba

DSIN [65]
IJCAI’19
Alibaba

DSTN [66]
KDD’19
Alibaba

MIMN [67]
KDD’19
Alibaba

BST [68]
DLP-KDD’19
Alibaba

GIN [69]
SIGIR’19
Alibaba

2018

DIN [70]
KDD’18
Alibaba

Multi-Task & Multi-Domain#

2023

SATrans [71]
KDD’23
Tencent

DFFM [72]
CIKM’23
Huawei

2022

M2M [73]
WSDM’22
Alibaba

2021

STAR [74]
CIKM’21
Alibaba

DASL [75]
KDD’21
Alibaba

GemNN [76]
SIGIR’21
Baidu

MTMS [77]
BigData’21
Baidu

2019

DeepMCP [78]
IJCAI’19
Alibaba

Embedding Learning#

2021

AutoDis [79]
KDD’21
Huawei

DG-ENN [80]
KDD’21
Huawei

GME [81]
KDD’21
Alibaba

2019

MetaEmbedding [82]
SIGIR’19

Pre-training#

2023

MAP [83]
KDD’23
Huawei

BERT4CTR [84]
KDD’23
Microsoft

SUM [85]
Arxiv’23
Meta

UniM^2Rec [86]
Arxiv’23
Tencent

SGP [87]
AdKDD’23
Amazon

2022

GUIM [88]
Arxiv’22
Alibaba

References#

1

Jieming Zhu, Qinglin Jia, Guohao Cai, Quanyu Dai, Jingjie Li, Zhenhua Dong, Ruiming Tang, and Rui Zhang. FINAL: Factorized Interaction Layer for CTR Prediction. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23), 2006–2010. 2023.

2

Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, and Zhenhua Dong. FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction. In Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI '23), 4552–4560. 2023.

3

Zhen Tian, Ting Bai, Wayne Xin Zhao, Ji-Rong Wen, and Zhao Cao. EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23), 1376–1385. 2023.

4

Fangye Wang, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang, and Ning Gu. Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23), 2523–2533. 2023.

5

Pengtao Zhang and Junlin Zhang. MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23), 3154–3163. 2023.

6

YaChen Yan and Liubo Li. AdaEnsemble: Learning Adaptively Sparse Structured Ensemble Network for Click-Through Rate Prediction. In Proceedings of the Workshop on Data Mining for Online Advertising (AdKDD '23). 2023.

7

Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, and Ning Gu. Enhancing CTR Prediction with Context-Aware Feature Representation Learning. In The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22), 343–352. 2022.

8

Bencheng Yan, Pengjie Wang, Kai Zhang, Feng Li, Hongbo Deng, Jian Xu, and Bo Zheng. APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction. In NeurIPS. 2022.

9

Zhishan Zhao, Sen Yang, Guohui Liu, Dawei Feng, and Kele Xu. FINT: Field-aware INTeraction Neural Network For CTR Prediction. CoRR, 2021.

10

Buyun Zhang, Liang Luo, Xi Liu, Jay Li, Zeliang Chen, Weilin Zhang, Xiaohan Wei, Yuchen Hao, Michael Tsang, Wenjun Wang, Yang Liu, Huayu Li, Yasmine Badr, Jongsoo Park, Jiyan Yang, Dheevatsa Mudigere, and Ellie Wen. DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction. In Proceedings of the 4th Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD (DLP-KDD '22). 2022.

11

Ruoxi Wang, Rakesh Shivanna, Derek Zhiyuan Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed H. Chi. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems. In The Web Conference (WWW '21), 1785–1797. 2021.

12

Yang Sun, Junwei Pan, Alex Zhang, and Aaron Flores. FM2: Field-matrixed Factorization Machines for Recommender Systems. In The Web Conference (WWW '21), 2828–2837. 2021.

13

Bo Chen, Yichao Wang, Zhirong Liu, Ruiming Tang, Wei Guo, Hongkun Zheng, Weiwei Yao, Muyu Zhang, and Xiuqiang He. Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models. In The 30th ACM International Conference on Information and Knowledge Management (CIKM '21), 3757–3766. 2021.

14

Yichen Xu, Yanqiao Zhu, Feng Yu, Qiang Liu, and Shu Wu. Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction. In The 30th ACM International Conference on Information and Knowledge Management (CIKM '21), 3553–3557. 2021.

15

Yuan Cheng and Yanbo Xue. Looking at CTR Prediction Again: Is Attention All You Need? In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21), 1279–1287. 2021.

16

Feng Li, Bencheng Yan, Qingqing Long, Pengjie Wang, Wei Lin, Jian Xu, and Bo Zheng. Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21), 2161–2165. 2021.

17

Gangwei Jiang, Hao Wang, Jin Chen, Haoyu Wang, Defu Lian, and Enhong Chen. xLightFM: Extremely Memory-Efficient Factorization Machine. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21), 337–346. 2021.

18

Lang Lang, Zhenlong Zhu, Xuanye Liu, Jianxin Zhao, Jixing Xu, and Minghui Shan. Architecture and Operation Adaptive Network for Online Recommendations. In The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), 3139–3149. 2021.

19

Zekai Chen, Fangtian Zhong, Zhumin Chen, Xiao Zhang, Robert Pless, and Xiuzhen Cheng. DCAP: Deep Cross Attentional Product Network for User Response Prediction. In The 30th ACM International Conference on Information and Knowledge Management (CIKM '21), 221–230. 2021.

20

YaChen Yan and Liubo Li. xDeepInt: A Hybrid Architecture for Modeling the Vector-wise and Bit-wise Feature Interactions. In Proceedings of the 3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD (DLP-KDD '21). 2021.

21

Weiyu Cheng, Yanyan Shen, and Linpeng Huang. Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI '20), 3609–3616. 2020.

22

Feng Yu, Zhaocheng Liu, Qiang Liu, Haoli Zhang, Shu Wu, and Liang Wang. Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions. In The 29th ACM International Conference on Information and Knowledge Management (CIKM '20), 2285–2288. 2020.

23

Bin Liu, Niannan Xue, Huifeng Guo, Ruiming Tang, Stefanos Zafeiriou, Xiuqiang He, and Zhenguo Li. AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval (SIGIR '20), 199–208. 2020.

24

Zhibin Li, Jian Zhang, Yongshun Gong, Yazhou Yao, and Qiang Wu. Field-wise Learning for Multi-field Categorical Data. In Annual Conference on Neural Information Processing Systems (NeurIPS '20). 2020.

25

Yi Yang, Baile Xu, Shaofeng Shen, Furao Shen, and Jian Zhao. Operation-aware Neural Networks for user response prediction. Neural Networks, 121:161–168, 2020.

26

Wantong Lu, Yantao Yu, Yongzhe Chang, Zhen Wang, Chenhui Li, and Bo Yuan. A Dual Input-aware Factorization Machine for CTR Prediction. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI '20), 3139–3145. 2020.

27

Bin Liu, Chenxu Zhu, Guilin Li, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, and Yong Yu. AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction. In The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '20), 2636–2645. 2020.

28

Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, and Xia Hu. Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction. In The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '20), 945–955. 2020.

29

Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, and Yan Liu. Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection. In The 8th International Conference on Learning Representations (ICLR '20). 2020.

30

Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM '19), 1161–1170. 2019.

31

Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, and Liang Wang. 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 (CIKM '19), 539–548. 2019.

32

Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, and Yuzhou Zhang. Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction. In The World Wide Web Conference (WWW '19), 1119–1129. 2019.

33

Tongwen Huang, Zhiqi Zhang, and Junlin Zhang. FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys '19), 169–177. 2019.

34

Yi Tay, Shuai Zhang, Anh Tuan Luu, Siu Cheung Hui, Lina Yao, and Tran Dang Quang Vinh. Holographic Factorization Machines for Recommendation. In The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI '19), 5143–5150. 2019.

35

Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, and Misha Smelyanskiy. Deep Learning Recommendation Model for Personalization and Recommendation Systems. CoRR, 2019.

36

Yantao Yu, Zhen Wang, and Bo Yuan. An Input-aware Factorization Machine for Sparse Prediction. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI '19), 1466–1472. 2019.

37

Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, and Quan Lu. Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising. In Proceedings of the 2018 World Wide Web Conference on World Wide Web (WWW '18), 1349–1357. 2018.

38

Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18), 1754–1763. 2018.

39

Xiangnan He and Tat-Seng Chua. Neural Factorization Machines for Sparse Predictive Analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17), 355–364. 2017.

40

Yuchin Juan, Damien Lefortier, and Olivier Chapelle. Field-aware Factorization Machines in a Real-world Online Advertising System. In Proceedings of the 26th International Conference on World Wide Web Companion (WWW '17), 680–688. 2017.

41

Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. Deep & Cross Network for Ad Click Predictions. In Proceedings of the Workshop on Data Mining for Online Advertising (AdKDD '17), 12:1–12:7. 2017.

42

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI '17), 1725–1731. 2017.

43

Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI '17), 3119–3125. 2017.

44

Yu-Chin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. Field-aware Factorization Machines for CTR Prediction. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16), 43–50. 2016.

45

Paul Covington, Jay Adams, and Emre Sargin. Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16), 191–198. 2016.

46

Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. Product-Based Neural Networks for User Response Prediction. In IEEE 16th International Conference on Data Mining (ICDM '16), 1149–1154. 2016.

47

Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. Wide & Deep Learning for Recommender Systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (DLRS@RecSys '16), 7–10. 2016.

48

Ying Shan, T. Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and J. C. Mao. Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16), 255–262. 2016.

49

Mathieu Blondel, Akinori Fujino, Naonori Ueda, and Masakazu Ishihata. Higher-Order Factorization Machines. In Annual Conference on Neural Information Processing Systems (NeurIPS '16), 3351–3359. 2016.

50

Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, and Xian-Sheng Hua. Deep CTR Prediction in Display Advertising. In Proceedings of the 2016 ACM Conference on Multimedia Conference (MM '16), 811–820. 2016.

51

Qiang Liu, Feng Yu, Shu Wu, and Liang Wang. A Convolutional Click Prediction Model. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM '15), 1743–1746. 2015.

52

Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, and Joaquin Quiñonero Candela. Practical Lessons from Predicting Clicks on Ads at Facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising (ADKDD '14), 5:1–5:9. 2014.

53

H. Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, and Jeremy Kubica. Ad Click Prediction: A View from the Trenches. In The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '13), 1222–1230. 2013.

54

Steffen Rendle. Factorization Machines. In The 10th IEEE International Conference on Data Mining (ICDM '10), 995–1000. 2010.

55

Matthew Richardson, Ewa Dominowska, and Robert Ragno. Predicting Clicks: Estimating the Click-through Rate for New Ads. In Proceedings of the 16th International Conference on World Wide Web (WWW '07), 521–530. 2007.

56

Jianxin Chang, Chenbin Zhang, Zhiyi Fu, Xiaoxue Zang, Lin Guan, Jing Lu, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song, and Kun Gai. TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23), 3785–3794. 2023.

57

Xuyang Hou, Zhe Wang, Qi Liu, Tan Qu, Jia Cheng, and Jun Lei. Deep Context Interest Network for Click-Through Rate Prediction. In The ACM International Conference on Information and Knowledge Management (CIKM '23). 2023.

58

Yue Cao, Xiaojiang Zhou, Jiaqi Feng, Peihao Huang, Yao Xiao, Dayao Chen, and Sheng Chen. Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM '22), 2974–2983. 2022.

59

Keke Zhao, Xing Zhao, Qi Cao, and Linjian Mo. A Non-sequential Approach to Deep User Interest Model for CTR Prediction. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM '22), 531–539. 2022.

60

En Xu, Zhiwen Yu, Bin Guo, and Helei Cui. Core Interest Network for Click-Through Rate Prediction. ACM Trans. Knowl. Discov. Data, 15(2):23:1–23:16, 2021.

61

Li He, Hongxu Chen, Dingxian Wang, Shoaib Jameel, Philip S. Yu, and Guandong Xu. Click-Through Rate Prediction with Multi-Modal Hypergraphs. In The 30th ACM International Conference on Information and Knowledge Management (CIKM '21), 690–699. 2021.

62

Zhibo Xiao, Luwei Yang, Wen Jiang, Yi Wei, Yi Hu, and Hao Wang. Deep Multi-Interest Network for Click-through Rate Prediction. In The 29th ACM International Conference on Information and Knowledge Management (CIKM '20), 2265–2268. 2020.

63

Xiang Li, Chao Wang, Jiwei Tan, Xiaoyi Zeng, Dan Ou, and Bo Zheng. Adversarial Multimodal Representation Learning for Click-Through Rate Prediction. In The Web Conference (WWW '20), 827–836. 2020.

64

Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. Deep Interest Evolution Network for Click-Through Rate Prediction. In The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI '19), 5941–5948. 2019.

65

Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang. Deep Session Interest Network for Click-Through Rate Prediction. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI '19), 2301–2307. 2019.

66

Wentao Ouyang, Xiuwu Zhang, Li Li, Heng Zou, Xin Xing, Zhaojie Liu, and Yanlong Du. Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19), 2078–2086. 2019.

67

Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, and Kun Gai. Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19), 2671–2679. 2019.

68

Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou. Behavior Sequence Transformer for E-commerce Recommendation in Alibaba. CoRR, 2019.

69

Feng Li, Zhenrui Chen, Pengjie Wang, Yi Ren, Di Zhang, and Xiaoyu Zhu. Graph Intention Network for Click-through Rate Prediction in Sponsored Search. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '19), 961–964. 2019.

70

Guorui Zhou, Xiaoqiang Zhu, Chengru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. Deep Interest Network for Click-Through Rate Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18), 1059–1068. 2018.

71

Erxue Min, Da Luo, Kangyi Lin, Chunzhen Huang, and Yang Liu. Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23), 4661–4672. 2023.

72

Wei Guo, Chenxu Zhu, Fan Yan, Bo Chen, Weiwen Liu, Huifeng Guo, Hongkun Zheng, Yong Liu, and Ruiming Tang. DFFM: Domain Facilitated Feature Modeling for CTR Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23), 4602–4608. 2023.

73

Qianqian Zhang, Xinru Liao, Quan Liu, Jian Xu, and Bo Zheng. Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling. In The Fifteenth ACM International Conference on Web Search and Data Mining (WSDM '22), 1368–1376. 2022.

74

Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, and Xiaoqiang Zhu. One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction. In The 30th ACM International Conference on Information and Knowledge Management (CIKM '21), 4104–4113. 2021.

75

Pan Li, Zhichao Jiang, Maofei Que, Yao Hu, and Alexander Tuzhilin. Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction. In The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), 3172–3180. 2021.

76

Hongliang Fei, Jingyuan Zhang, Xingxuan Zhou, Junhao Zhao, Xinyang Qi, and Ping Li. GemNN: Gating-enhanced Multi-task Neural Networks with Feature Interaction Learning for CTR Prediction. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21), 2166–2171. 2021.

77

Shulong Tan, Meifang Li, Weijie Zhao, Yandan Zheng, Xin Pei, and Ping Li. Multi-Task and Multi-Scene Unified Ranking Model for Online Advertising. In IEEE International Conference on Big Data (BigData '21), 2046–2051. 2021.

78

Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Chao Qi, Zhaojie Liu, and Yanlong Du. Representation Learning-Assisted Click-Through Rate Prediction. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI '19), 4561–4567. 2019.

79

Huifeng Guo, Bo Chen, Ruiming Tang, Weinan Zhang, Zhenguo Li, and Xiuqiang He. An Embedding Learning Framework for Numerical Features in CTR Prediction. In The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), 2910–2918. 2021.

80

Wei Guo, Rong Su, Renhao Tan, Huifeng Guo, Yingxue Zhang, Zhirong Liu, Ruiming Tang, and Xiuqiang He. Dual Graph enhanced Embedding Neural Network for CTR Prediction. In The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), 496–504. 2021.

81

Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Li Li, Kun Zhang, Jinmei Luo, Zhaojie Liu, and Yanlong Du. Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21), 1157–1166. 2021.

82

Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, and Qing He. Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '19), 695–704. 2019.

83

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