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      A New Approach for Advertising CTR Prediction Based on Deep Neural Network via Attention Mechanism

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          Abstract

          Click-through rate prediction is critical in Internet advertising and affects web publisher's profits and advertiser's payment. 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 in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition, takes advantage of the attention mechanism in neural network modelling, and improves FM to make feature interactions contribute differently to the prediction. We utilize stack autoencoder to explore high-order feature interactions and use improved FM for low-order feature interactions to portray the nonlinear associated relationship of features. The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in Internet advertising.

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          Most cited references 27

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          Wide & Deep Learning for Recommender Systems

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            Ad click prediction

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              A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval

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                Author and article information

                Contributors
                Journal
                Comput Math Methods Med
                Comput Math Methods Med
                CMMM
                Computational and Mathematical Methods in Medicine
                Hindawi
                1748-670X
                1748-6718
                2018
                13 September 2018
                : 2018
                Affiliations
                1School of Information Science and Engineering, Shandong Normal University, Jinan, China
                2School of Mathematical Science, Shandong Normal University, Jinan, China
                Author notes

                Academic Editor: Martti Juhola

                Article
                10.1155/2018/8056541
                6158939
                Copyright © 2018 Qianqian Wang et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 61572301
                Award ID: 61772321
                Funded by: Innovation Foundation of Science and Technology Development Center of Ministry of Education and New H3C Group
                Award ID: 2017A15047
                Funded by: CERNET Innovation Project
                Award ID: NGII20170508
                Funded by: Shandong Provincial Key Laboratory of Computer Network
                Award ID: SDKLCN-2016-01
                Categories
                Research Article

                Applied mathematics

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