Xiujin Shi
ABSTRACT
Ad click prediction is a task to estimate the click-through rate(CTR) in sponsored ads, the accuracy of which impacts user search experience and businesses' revenue. This challenging problem plays a key role in online advertising system and has to deal with several hard issues. State-of-the-art sponsored search systems generally formulate it as a supervised classification problem and employ machine learning approaches to predict the CTR per ad. However, the large-scale number of data leads to memory busy when training model on a single machine. Constantly, data scientists sample the huge data to scale down the calculating time which may circumvent the fitting result. In this paper, we propose a embedded model named XG-FwFMs which use less parameters calculating and prevent the model from over-fitting. Experimental results on real advertising data sets show that this approach has better prediction accuracy, parameter sensitivity and effectiveness than traditional nonlinear models.
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Index Terms
- An Embedded Model XG-FwFMs for Click-Through Rate
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