Search Results for author: Wentao Ouyang

Found 8 papers, 8 papers with code

Contrastive Learning for Conversion Rate Prediction

1 code implementation12 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

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

1 code implementation7 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

A Non-negative Symmetric Encoder-Decoder Approach for Community Detection

1 code implementation CIKM 2019 Bing-Jie Sun, Hua-Wei Shen, Jinhua Gao, Wentao Ouyang, Xue-Qi Cheng

Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes.

Clustering Community Detection +3

Click-Through Rate Prediction with the User Memory Network

1 code implementation9 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

Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction

1 code implementation10 Jun 2019 Wentao Ouyang, Xiuwu Zhang, Li Li, Heng Zou, Xin Xing, Zhaojie Liu, Yanlong Du

The intuitions are that ads shown together may influence each other, clicked ads reflect a user's preferences, and unclicked ads may indicate what a user dislikes to certain extent.

Click-Through Rate Prediction Test

DeepHawkes: Bridging the gap between prediction and understanding of information cascades

1 code implementation CIKM 2017 Qi Cao, HuaWei Shen, Keting Cen, Wentao Ouyang, Xueqi Cheng

In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade.

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