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Research on CTR prediction based on stacked autoencoder

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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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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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Correspondence to Fang’ai Liu.

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The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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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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