The Prediction of CTR Based on Model Fusion Theory
Online advertising makes it possible to show different ads to different customer groups according to their own characteristics, which will definitely prove the efficiency of ads, and we manage to accurate advertising by predicting the CTR of ads based on varieties of algorithm and models. This essay presented a kind of merged model of GBDT and LR, whose accuracy doesn’t heavily depend on the effect of building features artificially. In the GBDT part of the new model, the ways to build the decision trees made it possible to recognize the effective combination of features, on the other hand, the LR part of model makes it possible to deal with large amount of data. At the same test condition, the new model performed better than LR at the range of 1.41% to 1.75% with the standard of MSE, AUC and Log Loss. The results of the experiment show that GBDT model did a great job on building features for LR model without much help from human, which provides a new thought to improve the current CTR prediction models.
KeywordsCTR prediction Gradient Boosting Decision Trees Logistic Regression Model fusion
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