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

The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning

no code implementations • 8 Apr 2023 • Ruiqiang Liu, Qiqiang Zhong, Mengmeng Cui, Hanjie Mai, Qiang Zhang, Shaohua Xu, Xiangzheng Liu, Yanlong Du

The model uses a generative model to generate corresponding complement sentences and uses the contrastive learning method to guide the model to obtain more semantically meaningful encoding of the original sentence.

Contrastive Learning Sentence +1