因子交互全连接网络,因子分解机,压缩交互网络,神经网络," /> 因子交互全连接网络,因子分解机,压缩交互网络,神经网络,"/> factor interaction fully connected network,factorization machine,compressed interactive network,deep neural network,"/> <p class="MsoNormal"> 基于淘宝广告数据的点击概率预估模型研究
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沈阳化工大学学报, 2022, 36(5): 461-467    doi: 10.3969/j.issn.2095-2198.2022.05.011
  信息与计算机工程 本期目录 | 过刊浏览 | 高级检索 |

基于淘宝广告数据的点击概率预估模型研究

沈阳化工大学 化学工程学院,辽宁 沈阳 110142;

沈阳化工大学 计算机科学与技术学院, 辽宁 沈阳 110142

Research on Click Probability Estimation Model Based on Taobao Advertising Data

(Shenyang University of Chemical Technology, Shenyang 110142, China)

(Shenyang University of Chemical Technology, Shenyang 110142, China)

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摘要 

提出一种将压缩交互网络、因子分解机模型和神经网络3种模型相结合构成的因子交互全连接网络预估模型预测淘宝广告数据中用户对广告的点击概率.利用TensorFlow搭建整个算法模型,并使用淘宝广告展示数据集进行训练.最终训练出的模型可以得到数据中用户对任意一条广告的点击概率.采用曲线下面积(AUC)与对数损失函数(Logloss)值作为模型的评价指标,得到的结果与LRFMDeepFM等点击概率预估模型进行对比,AUC值提高了005Logloss值降低了004,效果得到明显提升.

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关键词:  因子交互全连接网络')" href="#">

因子交互全连接网络  因子分解机  压缩交互网络  神经网络    

Abstract: 

This paper presents a factor interactive fully connected network prediction model which combines compressed interactive network,factor decomposition machine model and neural network to predict the click probability of users in Taobao advertising data.Tensorflow is used to build the whole algorithm model,and Taobao advertising display data set is used for training.Finally,the trained model can get the click probability of any advertisement in the data.The area under the curve (AUC) and log loss function (Logloss) values are used as the evaluation indicators of the model.The results obtained are compared with LR,FM,DeepFM and other click probability prediction models.The AUC value is increased by 005,and the Logloss value is reduced by 004.The effect is significantly improved.

Key words:  factor interaction fully connected network')" href="#">

factor interaction fully connected network    factorization machine    compressed interactive network    deep neural network

               出版日期:  2022-10-30      发布日期:  2024-03-24      整期出版日期:  2022-10-30
ZTFLH: 

TP391 

 
  1  
基金资助: 

辽宁省教育厅科学技术研究项目(L2016011); 辽宁省教育厅科学研究项目(LQ2017008); 辽宁省博士启动基金项目(201601196)

作者简介:  高巍(1965—),女,辽宁沈阳人,教授,博士,主要从事大数据分析及应用、智能信息处理研究.
引用本文:    
高巍, 张奥南, 李大舟, 王淮中.

基于淘宝广告数据的点击概率预估模型研究 [J]. 沈阳化工大学学报, 2022, 36(5): 461-467.
GAO Wei, ZHANG Ao-nan, LI Da-zhou, WANG Huai-zhong.

Research on Click Probability Estimation Model Based on Taobao Advertising Data . Journal of Shenyang University of Chemical Technology, 2022, 36(5): 461-467.

链接本文:  
https://xuebao.syuct.edu.cn/CN/10.3969/j.issn.2095-2198.2022.05.011  或          https://xuebao.syuct.edu.cn/CN/Y2022/V36/I5/461

1HAZAN E,AGARWAL A,KALE S.Logarithmic Regret Algorithms for Online Convex OptimizationJ.Machine Learning,2007,69(2):169-192.

2RUDER S.An Overview of Gradient Descent Optimization AlgorithmsEB/OL.(2017-06-15)2020-09-05.https://arxiv.org/abs/1609.04747.

3RENDLE S.Factorization MachinesC//2010 IEEE International Conference on Data Mining.Piscataway:IEEE,2010:995-1000.

4ZHANG T,MENG S.Internet Financial Credit Evaluation Based on the Fusion of GBDT and LRC//Proceedings of the 2018 International Conference on Management,Economics,Education and Social Sciences(MEESS 2018).Amsterdam:Atlantis Press,2018:86-91.

5GUO H F,TANG R M,YE Y M,et al.DeepFM:a Factorization-Machine Based Neural Network for CTR PredictionC//Proceedings of the 26th International Joint Conference on Artificial Intelligence.Palo Alto:AAAI Press,2017:1725-1731.

6QU Y R,CAI H,REN K,et al.Product-Based Neural Networks for User Response PredictionC//2016 IEEE 16th International Conference on Data Mining (ICDM).Piscataway:IEEE,2016:1149-1154.

7QU Y R,FANG B H,ZHANG W N,et al.Product-Based Neural Networks for User Response Prediction Over Multi-Field Categorical DataJ.ACM Transactions on Information Systems,2018,37(1):1-35.

8COVINGTON P,ADAMS J,SARGIN E.Deep Neural Networks for Youtube RecommendationsC//Proceedings of the 10th ACM Conference on Recommender Systems.New York:Association for Computing Machinery,2016:191-198.

9CHENG H T,KOC L,HARMSEN J,et al.Wide & Deep Learning for Recommender SystemsC//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems.New York:Association for Computing Machinery,2016:7-10.

10ZHANG W N,DU T M,WANG J.Deep Learning Over Multi-Field Categorical Data——a Case Study on User Response PredictionM//FERRO JP3N,CRESTANI F,MOENS M F,et al.Advances in Information Retrieval.Cham:Springer,2016:45-47.

11LIAN J K,ZHOU X H,ZHANG F Z,et al.xDeepFM:Combining Explicit and Implicit Feature Interactions for Recommender SystemsC//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:Association for Computing Machinery,2018:1754-1763.

12ZHOU G R,SONG C R,ZHU X Q,et al.Deep Interest Network for Click-Through Rate PredictionC//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:Association for Computing Machinery,2018:1059-1068.

13ZHENG Y,ZHANG Y J,LAROCHELLE H.A Deep and Autoregressive Approach for Topic Modeling of Multimodal DataJ.IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(6):1056-1069.

14CHEN J X,SUN B G,LI H,et al.Deep CTR Prediction in Display AdvertisingC//Proceedings of the 24th ACM International Conference on Multimedia.New York:Association for Computing Machinery,2016:811-820.

15JUAN Y,ZHUANG Y,CHIN W S,et al.Field-Aware Factorization Machines for CTR PredictionC//Proceedings of the 10th ACM Conference on Recommender Systems.New York:Association for Computing Machinery,2016:43-50.

16LIU Q,YU F,WU S,et al.A Convolutional Click Prediction ModelC//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management.New York:Association for Computing Machinery,2015:1743-1746.

17MCMAHAN H B,HOLT G,SCULLEY D,et al.Ad Click Prediction:a View from the TrenchesC//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:Association for Computing Machinery,2013:1222-1230.

18ZHANG Y Y,DAI H J,XU C,et al.Sequential Click Prediction for Sponsored Search with Recurrent Neural NetworksEB/OL.(2014-07-28)2020-01-05.https://arxiv.org/abs/1404.5772.

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