CTR prediction for online advertising based on a features conjunction model
SHEN Fangyao1, DAI Guojun1, DAI Chenglei2, GUO Hongjie3, ZHANG Hua1
1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; 2. Department of Mathematical Science, Zhejiang University, Hangzhou 310058, China; 3. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Abstract：Click-through rate (CTR) predictions are important for internet companies. The CTR is closely related to the context, user attributes and advertising attributes, with effective CTR predictions essential for improving company revenue. The traditional LR model was optimized to predict the relationship between the user and advertiser characteristics for the CTR which were added to the Sigmoid function to obtain a new features conjunction model. The online optimization algorithm follow-the-regularized-leader (FTRL) was used to improve the efficiency of the parameter, and the mixed regularization was used to prevent over fitting. Tests on a real-world advertising dataset show that this method has better accuracy, efficiency, parameter sensitivity and reliability compared with previous algorithms.
沈方瑶, 戴国骏, 代成雷, 郭鸿杰, 张桦. 基于特征关联模型的广告点击率预测[J]. 清华大学学报（自然科学版）, 2018, 58(4): 374-379.
SHEN Fangyao, DAI Guojun, DAI Chenglei, GUO Hongjie, ZHANG Hua. CTR prediction for online advertising based on a features conjunction model. Journal of Tsinghua University(Science and Technology), 2018, 58(4): 374-379.
[1] CHAPELLE O, MANAVOGLU E, ROSALES R. Simple and scalable response prediction for display advertising[J]. ACM Transactions on Intelligent Systems and Technology, 2015, 5(4):1-34. [2] AGARWAL A, CHAPELLE O, DUDÍK M, et al. A reliable effective terascale linear learning system[J]. The Journal of Machine Learning Research, 2014, 15(1):1111-1133. [3] 黄璐, 林川杰, 何军, 等. 融合主题模型和协同过滤的多样化移动应用推荐[J]. 软件学报, 2017, 28(3):708-720. HUANG L, LIN C J, HE J, et al. Diversified mobile app recommendation combining topic model and collaborative filtering[J]. Journal of Software, 2017, 28(3):708-720. (in Chinese) [4] GRAEPEL T, CANDELA J Q, BORCHERT T, et al. Web-scale Bayesian click-through rate prediction for sponsored search advertising in microsoft's bing search engine[C]//Proceedings of the 27th International Conference on Machine Learning. Haifa, Israel:ACM, 2010:13-20. [5] MA J, SAUL L K, SAVAGE S, et al. Learning to detect malicious URLs[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):1-24. [6] BAI Y Q, SHEN K J. Alternating direction method of multipliers for L1-L2-regularized logistic regression model[J]. Journal of the Operations Research Society of China, 2016, 4(2):243-253. [7] QUAN D Y, YIN L H, GUO Y C. Assessing the disclosure of user profile in mobile-aware services[C]//Proceedings of the 11th International Conference on Information Security and Cryptology. Beijing, China:Springer, 2015:451-467. [8] ZINKEVICH M. Online convex programming and generalized infinitesimal gradient ascent[C]//Proceedings of the 20th International Conference on Machine Learning. Washington, USA:AAIA, 2003:928-936. [9] LANGFORD J, LI L H, ZHANG T. Sparse online learning via truncated gradient[J]. The Journal of Machine Learning Research, 2009, 10:777-801. [10] DUCHI J, SINGER Y. Efficient online and batch learning using forward backward splitting[J]. The Journal of Machine Learning Research, 2009, 10:2899-2934. [11] LIN X. Dual averaging methods for regularized stochastic learning and online optimization[J]. The Journal of Machine Learning Research, 2010, 11:2543-2596. [12] MCMAHAN H B, HOLT G, SCULLEY D, et al. Ad click prediction:A view from the trenches[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Chicago, USA:ACM, 2013:1222-1230. [13] MCMAHAN H B. Follow-the-regularized-leader and mirror descent:Equivalence theorems and L1-regularization[C]//Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. Fort Lauderdale, USA:JMLR, 2011:525-533. [14] BILGIC B, CHATNUNTAWECH I, FAN A P, et al. Fast image reconstruction with L2-regularization[J]. Journal of Magnetic Resonance Imaging, 2014, 40(1):181-191. [15] TIBSHIRANI R. Regression shrinkage and selection via the Lasso[J]. Journal of the Royal Statistical Society, 1996, 58(1):267-288. [16] YAN L, LI W J, XUE R G, et al. Coupled group lasso for web-scale CTR prediction in display advertising[C]//Proceedings of the 31st International Conference on Machine Learning. Beijing, China:ACM, 2014:802-810.
2018, Vol. 58 
