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.

Contrastive Learning for Conversion Rate Prediction

1 code implementation • 12 Jul 2023 • Wentao Ouyang, Rui Dong, Xiuwu Zhang, Chaofeng Guo, Jinmei Luo, Xiangzheng Liu, Yanlong Du

To tailor the contrastive learning task to the CVR prediction problem, we propose embedding masking (EM), rather than feature masking, to create two views of augmented samples.

Contrastive Learning

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

MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction

1 code implementation • 7 Aug 2020 • Wentao Ouyang, Xiuwu Zhang, Lei Zhao, Jinmei Luo, Yu Zhang, Heng Zou, Zhaojie Liu, Yanlong Du

Our study is based on UC Toutiao (a news feed service integrated with the UC Browser App, serving hundreds of millions of users daily), where the source domain is the news and the target domain is the ad.

Click-Through Rate Prediction