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.
Highlights
Click-through rate (CTR) prediction is critical to many web applications including web search, recommender systems [1, 2], sponsored search, and display advertising
To reduce the high sparseness of features and characterize the nonlinear association between features, we propose a sparse feature learning method for advertising data based on deep learning (DLSAE)
We compare the Attention Stacked Autoencoder (ASAE) model with the following methods that are designed for sparse data prediction: Factorization machines (FMs) [34]: FM is successfully applied to the recommended system and user response prediction task
Summary
Click-through rate (CTR) prediction is critical to many web applications including web search, recommender systems [1, 2], sponsored search, and display advertising. In the cost-perclick model, the advertiser pays the web publisher only when a user clicks their advertisements and visits the advertiser’s site. E CTR prediction is defined to estimate the ratio of clicks to impressions of advertisements that will be displayed [3]. With the rapid development of the mobile Internet and its wide range of applications, advertising has become one of the most successful business models in the world. Internet text advertising is regarded as a more effective advertising communication method due to its strong targeted communication and convenience of user clicking and has become an important income resource for many
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