Abstract
Click-through rate prediction is critical in internet advertising and affects web publisher’s profits and advertiser’s payment. In the CTR prediction, mining the interaction between features and extracting user interest are key factors affecting the prediction rate. The traditional method of obtaining features using feature extraction did not consider the sparseness of advertising data and the highly nonlinear association between features. To reduce the sparseness of data and to mine the hidden features and user interest in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition, and uses the bidirectional gated recurrent unit (Bi-GRU) to extract user interest. We utilize stacked autoencoder to portray the nonlinear associated relationship of data. The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in internet advertising.
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Cheng HT, Koc L, Harmsen J, et al (2016) Wide & deep learning for recommender systems[C]// The Workshop on Deep Learning for Recommender Systems. ACM, p 7–10
Chapelle O, Rosales R, Rosales R (2015) Simple and scalable response prediction for display advertising[M]. ACM
Graepel T, Borchert T, Herbrich R (2010) Web-scale Bayesian click-through rate prediction for sponsored search advertising in Microsoft’s bing search engine[C]// International Conference on International Conference on Machine Learning. Omnipress, p 13–20
Richardson M, Dominowska E, Ragno R (2007) Predicting clicks: estimating the click-through rate for new ads. Proceedings of the 16th international conference on World Wide Web. ACM
Zhou AY, Zhou MQ, Gong XQ (2011) Computational advertising: A data-centric comprehensive web application[J]. Jisuanji Xuebao(Chinese Journal of Computers) 34(10):1805–1819
Genzel M, Kutyniok G (2016) A mathematical framework for feature selection from real-world data with non-linear observations[J]
Wang X, Li W, Cui Y, et al (2011) Click-through rate estimation for rare events in online advertising[J]. Online Multimedia Advertising Techniques & Technologies
Mcmahan HB, Holt G, Sculley D, et al (2013) Ad click prediction: a view from the trenches[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, p 1222–1230
Chapelle O (2014) Modeling delayed feedback in display advertising[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, p 1097–1105
He X, et al (2014) Practical lessons from predicting clicks on ads at facebook. Proceedings of the Eighth International Workshop on Data Mining for Online Advertising. ACM
Shen S, Hu B, Chen W, et al (2012) Personalized click model through collaborative filtering[C]// ACM International Conference on Web Search and Data Mining. ACM, p 323–332
Rendle S (2012) Factorization machines with libfm. ACM Transactions on Intelligent Systems and Technology (TIST) 3.3:57
Baqapuri AI, Trofimov I (2014) Using neural networks for click prediction of sponsored search [J]. arXiv preprint arXiv:1412.6601
Kumar R, Naik SM, Naik VD, et al (2015) Predicting clicks: CTR estimation of advertisements using Logistic Regression classifier[C]// Advance Computing Conference. IEEE, p 1134–1138
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436
Chen LC, Papandreou G, Kokkinos I et al (2018) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE Trans Pattern Anal Mach Intell 40(4):834–848
Mnih V, Heess N, Graves A (2014) Recurrent models of visual attention[C]//Advances in neural information processing systems. p 2204–2212
Graves A, Mohamed A-r, Hinton G (2013) Speech recognition with deep recurrent neural networks. Acoustics, speech and signal processing (icassp), 2013 ieee international conference on. IEEE
Baqapuri AI, Trofimov I (2014) Using neural networks for click prediction of sponsored search. arXiv preprint arXiv:1412.6601
Liu Q, et al (2015) A convolutional click prediction model. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM
Zhang Y, Dai H, Xu C, et al (2014) Sequential click prediction for sponsored search with recurrent neural networks[C]//AAAI. 14:1369–1375
Zhang W, Du T, Wang J (2016) Deep learning over multi-field categorical data. European conference on information retrieval. Springer, Cham
Guo H, Tang R, Ye Y, et al. (2018) DeepFM: an end-to-end wide & deep learning framework for CTR prediction[J]. arXiv preprint arXiv:1804.04950
Hartigan JA, Wong MA (2010) A k-means clustering algorithm. Applied statistics [J]
Kolda T G, Sun J (2008) Scalable tensor decompositions for multi-aspect data mining[C]// Eighth IEEE International Conference on Data Mining. IEEE Computer Society, p 363–372
Szwabe A, Misiorek P, Ciesielczyk M (2017) Tensor-based modeling of temporal features for big data CTR estimation[C]// International Conference: Beyond Databases, Architectures and Structures. Springer, Cham, p 16–27
Cheng H (2010) Personalized click prediction in sponsored search[C]// ACM international conference on web search and data mining. ACM:351–360
Cho K, et al. (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078
Hinton GE, Zemel RS (1993) Autoencoders, minimum description length and Helmholtz free energy[C]// International Conference on Neural Information Processing Systems. Morgan Kaufmann Publishers Inc., p 3–10
Lu Y, Dong R, Smyth B (2018) Coevolutionary recommendation model: mutual learning between ratings and reviews. Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee
Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization[J]. J Mach Learn Res 12(7):257–269
Chin WS, Zhuang Y, Juan YC, et al (2015) A learning-rate schedule for stochastic gradient methods to matrix factorization[M]// Advances in Knowledge Discovery and Data Mining. Springer International Publishing, p 442–455
Qu Y, et al (2016) Product-based neural networks for user response prediction. Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE
Chen JH, Zhao ZQ, Shi JY et al (2017) A new approach for mobile advertising click-through rate estimation based on deep belief nets[J]. Computational Intelligence and Neuroscience 2017
He X, Chua TS (2017) Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, p 355–364
Akimova T, Levine MH, Beier UH, et al (2016) Standardization, evaluation, and area-under-curve analysis of human and murine Treg suppressive function[M]//Suppression and Regulation of Immune Responses. Humana Press, New York, p 43–78
Chen J, Zhang H, He X, et al (2017) Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, p 335–344
Xiao J, Ye H, He X, et al (2017) Attentional factorization machines: Learning the weight of feature interactions via attention networks[J]. arXiv preprint arXiv:1708.04617
Acknowledgments
This work was supported by the following grants: National Natural Science Foundation of China (No. 61572301, No. 61772321), the Innovation Fundation of Science and Technology Development Center of Ministry of Education and New H3C Group(2017A15047), Natural Science Foundation of Shandong Province (No. ZR2016FP07), the Open Research Fund from Shandong provincial Key Laboratory of Computer Network (No. SDKLCN-2016-01).
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Wang, Q., Liu, F., Xing, S. et al. Research on CTR prediction based on stacked autoencoder. Appl Intell 49, 2970–2981 (2019). https://doi.org/10.1007/s10489-019-01416-5
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DOI: https://doi.org/10.1007/s10489-019-01416-5