|Table of Contents|

Advertising click-through rate prediction modelbased on enhanced FNN(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2020年01期
Page:
33-39
Research Field:
Publishing date:

Info

Title:
Advertising click-through rate prediction modelbased on enhanced FNN
Author(s):
Yang YantingHan Bin
School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212003,China
Keywords:
click-through rate prediction feature combinations neural network feature generation
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2020.44.01.006
Abstract:
In order to further improve the ability of the click-through rate(CTR)prediction model to learn effective feature combinations,this paper proposes an advertising click-through rate prediction model based on enhanced factorization machine supported neural network(EFNN). This model adds a new feature generation layer to factorization machine supported neural network(FNN),and uses a convolution operation for CTR data. After channel transformation of the data,the inception structure is introduced for convolution. The generated new features and original features are combined to improve the learning ability of the deep network. The experimental results prove that the enhanced FNN with new feature generation layer can effectively improve the accuracy of advertising click-through rate prediction.

References:

[1] Richardson M,Dominowska E,Ragno R. Predicting clicks:estimating the click-through rate for new ads[C]//Proceedings of the 16th International Conference of World Wide Web. Banff Alberta,Canada:W3C,2007:521-530.
[2]匡俊,唐卫红,陈雷慧,等. 基于特征工程的视频点击率预测算法[J]. 华东师范大学学报(自然科学版),2018,2018(3):77-87.
Kuang Jun,Tang Weihong,Chen Leihui,et al. Algorithem for video click-through rate prediction[J]. Journal of East China Normal University(Natural Science),2018,2018(3):77-87.
[3]Rendle S. Factorization machines with libfm[J]. ACM Transactions on Intelligent Systems and Technology,2012,3(3):1-22.
[4]邓路佳,刘平山. 基于GMM-FMs的广告点击率预测研究[J]. 计算机工程,2019,45(5):122-126.
Deng Lujia,Liu Pingshan. Research on the prediction of advertising click rate based on GMM-FMs[J]. Computer Engineering,2019,45(5):122-126.
[5]Zhang Weinan,Du Tianming,Wang Jun. Deep learning over multi-field categorical data[C]//European Conference on Information Retrieval. Berlin,German:Springer,2016:45-57.
[6]Cheng H T,Koc L,Harmsen J,et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. New York,USA:ACM,2016:7-10.
[7]刘梦娟,曾贵川,岳威,等. 基于融合结构的在线广告点击率预测模型[J]. 计算机学报,2019,42(7):1-18.
Liu Mengjuan,Zeng Guichuan,Yue wei,et al. A hybrid network based CTR prediction model for online advertising[J]. Chinese Journal of Computers,2019,42(7):1-18.
[8]Guo Huifeng,Tang Ruiming,Ye Yunming,et al. Deepfm:a factorization-machine based neural network for CTR prediction[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. California,USA:Morgan Kaufmann,2017:1725-1731.
[9]Liu Qiang,Yu Feng,Wu Shu,et al. A convolutional click prediction model[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne,Australia:ACM,2015:1743-1746.
[10]练建勋. 基于多样化内容数据的个性化推荐系统[D]. 合肥:中国科学技术大学计算机科学与技术学院,2018:56-77.
[11]何柔萤,徐建. 基于注意力卷积神经网络的工作票专家推荐方法[J]. 南京理工大学学报,2019,43(1):13-21,47.
He Rouying,Xu Jian. Expert recommendation for trouble tickets using attention-based CNN model[J]. Journal of Nanjing University of Science and Technology,2019,43(1):13-21,47.
[12]厍向阳,王邵鹏. 基于卷积-LSTM网络的广告点击率预测模型研究[J]. 计算机工程与应用,2019,55(2):193-197.
She Xiangyang,Wang Shaopeng. Research on advertising click through rate prediction model based on CNN-LSTM network[J]. Computer Engineering and Applications,2019,55(2):193-197.
[13]Szegedy C,Liu Wei,Jia Yangqing,et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway,USA:IEEE,2015:1-12.
[14]叶锡君,袁培森,郭小清,等. 基于用户兴趣和项目周期的协同过滤推荐算法[J]. 南京理工大学学报,2018,42(4):392-400.
Ye Xijun,Yuan Peiseng,GuoXiaoqing,et al. Collaborative filtering recommendation algorithm based on user interest and project cycle[J]. Journal of Nanjing University of Science and Technology,2018,42(4):392-400.
[15]Zhou Guorui,Zhu Xiaoqiang,Song Chenru,et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York,USA:ACM,2018:1059-1068.
[16]Zhou Guorui,Mou Na,Fan Ying,et al. Deep interest evolution network for click-through rate prediction[C]//AAAI Conference on Artificial Intelligence. Palo Alto,USA:AAAI,2019:353-362.

Memo

Memo:
-
Last Update: 2020-02-29