Computer Science ›› 2024, Vol. 51 ›› Issue (2): 73-78.doi: 10.11896/jsjkx.230100052

• Database & Big Data & Data Science • Previous Articles     Next Articles

Logical Regression Click Prediction Algorithm Based on Combination Structure

GUO Shangzhi, LIAO Xiaofeng, XIAN Kaiyi   

  1. College of Computer Science,Chongqing University,Chongqing 400030,China
  • Received:2023-01-10 Revised:2023-05-22 Online:2024-02-15 Published:2024-02-22
  • About author:GUO Shangzhi,born in 1982,Ph.D,se-nior engineer.His main research intere-sts include artificial intelligence and intelligent manufacturing,intelligent recom-mendation,machine learning,and big data applications.

Abstract: With the rapid development of the Internet and advertising platforms,in the face of massive advertising information,in order to improve the user click rate,an improved logical regression click prediction algorithm,logical regression of combination structure(LRCS) based on composite structure is proposed.The algorithm is based on different types of features,which may have different audiences.First,FM is used to combine features to generate two types of combined features.Secondly,a kind of feature combination is used as clustering algorithm for clustering.Finally,another type of feature combination is input into the segmented GBDT+logical regression combination model generated by clustering for prediction.Through multi angle verification in two public datasets,and compared with other commonly used click prediction algorithms,it shows that LRCS has a certain performance improvement in click prediction.

Key words: Logical regression, Feature combination, Clustering, Combination recommendation, Artificial intelligence, Intelligent manufacturing

CLC Number: 

  • TP391
[1]FOO L K,CHUA S L,IBRAHIM N.Attribute weighted naïve bayes classifier[J].Computers,Materials & Continua,2022,71(1):1945-1957.
[2]HU R,ZHU X,ZHU Y,et al.Robust SVM with adaptive graph learning[J].World Wide Web,2020,23(3):1945-1968.
[3]SHERWIN J S,CHARTIER J.Parameter optimization of logistic regression classifiers[J].BMC Neuroscience,2013,14(1):1-2.
[4]TIAN X,WANG J,WEN Y,et al.Multi-attribute scientific docu-ments retrieval and ranking model based on GBDT and LR[J].Math.Biosci.Eng.,2022,19:3748-3766.
[5]GHARIBSHAH Z,ZHU X,HAINLINE M.Deep learning for user interest and response prediction in online display adverti-sing[J].Data Science and Engineering,2020,5(1):12-26.
[6]PI Q,BIAN W,ZHOU G,et al.Practice on long sequential user behavior modeling for click-through rate prediction[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2019:2671-2679.
[7]ZHOU G,ZHU X,SONG C,et al.Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2018:1059-1068.
[8]HUANG Q,XU Y Y,CHEN Y,et al.An Adaptive Mechanism for Recommendation Algorithm Ensemble[J].IEEE ACCESS,2019,7:10331-10342.
[9]SEDLMAIR M,MUNZNER T,TORY M.Empirical guidance on scatterplot and dimension reduction technique choices[J].IEEE Transactions on Visualization and Computer Graphics,2013,19(12):2634-2643.
[10]RENDLES.Factorization machines[C]//2010 IEEE Interna-tional Conference on Data Mining.IEEE,2010:995-1000.
[11]JUAN Y,ZHUANG Y,CHIN W S,et al.Field-aware factorization machines for CTR prediction[C]//Proceedings of the 10th ACM Conference on Recommender Systems.2016:43-50.
[12]GUO H,TANG R,YE Y.DeepFM:A Factorization-Machine-based Neural Network for CTR Prediction[J].Proceedings of the 26th International Joint Conference on Artificial Intelligence.Melbourne,Australia,2017:1725-1731.
[13]WANG R,FU B,FU G,et al.Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD’17.2017:1-7.
[14]GÜNER S,CODAL K S,GECER H S,et al.Using k-means clustering algorithm in the identification of traffic accident patterns: the application of Sakarya province[J].Journal of Business Science,2018,6(3):89-105.
[15]ISHIKAWA T,YATA N,NAGAO T.Automatic Classification of Paper Using Combinational Optimization of Image Features[J].Japan Tappi Journal,2011,65(6):595-604.
[16]XIAO J,YE H,HE X,et al.Attentional factorization machines:Learning the weight of feature interactions via attention networks[J].arXiv:1708.04617,2017.
[17]QU Y,CAIH R.Product-based neural networks for user re-sponse prediction[C]//Proceedings of the IEEE International Conference on DataMining.Barcelona,Spain,2016:6.
[18]LIU B,TANG R,CHEN Y,et al.Feature generation by convolutional neural network for click-through rate prediction[C]//The World Wide Web Conference.2019:1119-1129.
[19]HE X,CHUA T S.Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval.2017:355-364.
[20]MOSKVICHEV O,NIKISHCHENKOV S,MOSKVICHEVAE.Optimization of production and transport infrastructure based on cluster analysis methods[C]//E3S Web of Conferences.EDP Sciences,2020,164:03008.
[21]MARTÍNEZ-CEVALLOS D,PROAÑO-GRIJALVA A,AL-GUACIL M,et al.Segmentation of participants in a sports event using cluster analysis[J].Sustainability,2020,12(14):5641.
[22]AGARWAL N,HAQUE E,LIUH,et al.Research paper recommender systems:A subspace clustering approach[C]//International Conference on Web-Age Information Management.Berlin,Heidelberg:Springer,2005:475-491.
[23]SUN X H,ZHANG L.Collaborative filtering recommendationalgorithm based on scoring region subspace [J].Computer Science,2022,49(7):50-56.
[24]RISHICKESH R,SHAHINA A,NAYEEMULLA KHAN A.Predicting forest fires using supervised and ensemble machine learning algorithms[J]. International Journal Recent Technology Engineering,2019,8:3697-3705.
[25]DÉSIR C,BERNARD S,PETITJEAN C,et al.One class random forests[J].Pattern Recognition,2013,46(12):3490-3506.
[26]VANI M S,RAJASHREE S.Forecast of Mobile Ad ClickThrough Logistic Regression Algorithm[J].Journal of Innovation in Computer Science and Engineering,2016,6(1):29-32.
[27]WANG S,SUN G,LI Y.SVD++ recommendation algorithm based on backtracking[J].Information,2020,11(7):369.
[28]JUNG H G.Medoid selection from sub-tree leaf nodes for k-medoid clustering-based hierarchical template tree construction[J].Electronics Letters,2013,49(2):108-109.
[29]BIJALWAN A,PUROHIT K C,MALIK P,et al.A Self-Adap-table Angular Based K-Medoid Clustering Scheme(SAACS) for Dynamic VANETs[J].Electronics,2022,11(19):3071.
[30]LI J,HUANG Y,QIAO M,et al.Effects of water soaked height on the deformation and crushing characteristics of loose gangue backfill material in solid backfill coal mining[J].Processes,2018,6(6):64.
[31]WANG Q,LIU F,ZHAO X,et al.Session interest model forCTR prediction based on self-attention mechanism[J].Scientific Reports,2022,12(1):1-13.
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