Presentation information

General Session

General Session » [GS] J-2 Machine learning

[4O2-J-2] Machine learning: improvements of user satisfaction

Fri. Jun 7, 2019 12:00 PM - 1:20 PM Room O (Front-left room of 1F Exhibition hall)

Chair:Yoshifumi Seki Reviewer:Hidekazu Oiwa

12:20 PM - 12:40 PM

[4O2-J-2-02] Ad Click Prediction by using Domain Adaptation Neural Network

〇Kazuki Taniguchi1, Shota Yasui1 (1. CyberAgent,Inc.)

Keywords:Domain Adaptation, Advertising, Response Prediction

Online display advertising is one of the largest businesses for Internet companies and growing each year. Predict-
ing the probability of ad click is important for buyers to value bid requests in Real Time Bidding (RTB) setting.
Most of previous research use ad impressions, which happened only when the buyer wins in the auction, as a train-
ing data for click prediction. Such click prediction models, however, predict the probability of click not only for
impression but all bid requests in the real product. This gap suggests that the click prediction model trained with
impression data is suffered from selection bias. In this paper, we propose a new click prediction model that uses
domain adaptation neural network (DANN) to mitigate this problem. DANN can train both a label predictor and
the invariant features between domains at once. Experimental result shows that our proposed method improves
the accuracy of click prediction.