Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model

no code implementations • 26 Mar 2024 • Wentao Ouyang, Xiuwu Zhang, Chaofeng Guo, Shukui Ren, Yupei Sui, Kun Zhang, Jinmei Luo, Yunfeng Chen, Dongbo Xu, Xiangzheng Liu, Yanlong Du

A desired model for this problem should satisfy the following requirements: 1) Accuracy: the model should achieve fine-grained accuracy with respect to any conversion type in any display scenario.

Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction

1 code implementation • 19 May 2021 • Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Li Li, Kun Zhang, Jinmei Luo, Zhaojie Liu, Yanlong Du

For existing old ads, GMEs first build a graph to connect them with new ads, and then adaptively distill useful information.

Click-Through Rate Prediction Meta-Learning

Click-Through Rate Prediction with the User Memory Network

1 code implementation • 9 Jul 2019 • Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Li Li, Zhaojie Liu, Yanlong Du

Both offline and online experiments demonstrate the effectiveness of MA-DNN for practical CTR prediction services.

Click-Through Rate Prediction

Representation Learning-Assisted Click-Through Rate Prediction

1 code implementation • 11 Jun 2019 • Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Chao Qi, Zhaojie Liu, Yanlong Du

These subnets model the user-ad, ad-ad and feature-CTR relationship respectively.

Click-Through Rate Prediction Representation Learning