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A Convolutional Click Prediction Model
Liu, Qiang; Yu, Feng; Wu, Shu; Wang, Liang
Conference NameACM International Conference on Information and Knowledge Management (CIKM)
Source PublicationIn Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015
Conference DateOct 24-28
Conference PlaceMelbourne
AbstractThe explosion in online advertisement urges to better estimate the click prediction of ads. For click prediction on single ad impression, we have access to pairwise relevance among elements in an impression, but not to global interaction among key features of elements. Moreover, the existing method on sequential click prediction treats propagation unchangeable for different time intervals. In this work, we propose a novel model, Convolutional Click Prediction Model (CCPM), based on convolution neural network. CCPM can extract local-global key features from an input instance with varied elements, which can be implemented for not only single ad impression but also sequential ad impression. Experiment results on two public large-scale datasets indicate that CCPM is effective on click prediction.
KeywordClick Prediction Convolution Neural Network
Document Type会议论文
Corresponding AuthorWu, Shu
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Liu, Qiang,Yu, Feng,Wu, Shu,et al. A Convolutional Click Prediction Model[C],2015.
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