Recurrent Meta-Learning against Generalized Cold-start Problem in CTR Prediction

During the last decades, great success has been witnessed along the course of accurate Click-Through-Rate (CTR) prediction models for online advertising. However, the cold-start problem, which refers to the issue that the standard models can hardly draw accurate inferences for unseen users/ads, is still yet to be fully understood. Most recently, some related studies have been proposed to tackle this problem with only the new users/ads being considered. We argue that such new users/ads are not the only sources for cold-start. From another perspective, since users might shift their interests over time, one's recent behaviors might vary greatly from the records long ago. In this sense, we believe that the cold-start problem should also exist along the temporal dimension. Motivated by this, a generalized definition of the cold-start problem is provided where both new users/ads and recent behavioral data from known users are considered. To attack this problem, we propose a recursive meta-learning model with the user's behavior sequence prediction as a separate training task. Specifically, a time-series CTR model with the MAML (Model-Agnostic Meta-Learning)-like meta-learning method is proposed to make our model adapt to new tasks rapidly. Besides, we propose a parallel structure for extracting the feature interactions to efficiently fuse attention mechanisms and the RNN layer. Finally, experiments on three public datasets demonstrate the effectiveness of the proposed approaches.