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

Code

• github.com/2023-MindSpore-4/Code16/tree/main/yangzhiyong/recurrent_-meta_-learning_for_-ctr_mindspore
  mindspore★ 0

Abstract

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.

Tasks

Reproductions