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Boosted Trees Model with Feature Engineering
Guanglei Du (National University of Defense Technology), Chen Yang (National University of Defense Technology), Xu Wang (National University of Defense Technology)
Abstract: The post-click conversion rate (CVR) prediction is important for the sponsored search engine. The predicted CVR can guide people on how to choose the advertisements to display, and greatly affects the revenue of the electronic business platform and merchants who pay for the advertisement. In this paper, we describe a new approach to solve the CVR prediction problem in sponsored search. The approach is Boosted Trees Model with Feature Engineering (BTMFE), which incorporates the power of feature engineering as well as the boosted trees. We discuss different issues such as feature importance, feature interaction, historical information fusion and boosted trees algorithm. Finally, we compare BTMFE with other methods and present experimental results on real-world datasets.