Search Results for author: Shuguang Han

Found 12 papers, 4 papers with code

Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction

no code implementations9 Aug 2023 Yunfeng Zhao, Xu Yan, Xiaoqiang Gui, Shuguang Han, Xiang-Rong Sheng, Guoxian Yu, Jufeng Chen, Zhao Xu, Bo Zheng

Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training.

Recommendation Systems Selection bias

COPR: Consistency-Oriented Pre-Ranking for Online Advertising

no code implementations6 Jun 2023 Zhishan Zhao, Jingyue Gao, Yu Zhang, Shuguang Han, Siyuan Lou, Xiang-Rong Sheng, Zhe Wang, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng

In this architecture, the pre-ranking model is expected to be a lightweight approximation of the ranking model, which handles more candidates with strict latency requirements.

Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models

1 code implementation4 Sep 2022 Zhao-Yu Zhang, Xiang-Rong Sheng, Yujing Zhang, Biye Jiang, Shuguang Han, Hongbo Deng, Bo Zheng

However, far less attention has been paid to the overfitting problem of models in recommendation systems, which, on the contrary, is recognized as a critical issue for deep neural networks.

Click-Through Rate Prediction Recommendation Systems

KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging

no code implementations22 Aug 2022 Yujing Zhang, Zhangming Chan, Shuhao Xu, Weijie Bian, Shuguang Han, Hongbo Deng, Bo Zheng

To alleviate this issue, we propose to extract knowledge from the \textit{super-domain} that contains web-scale and long-time impression data, and further assist the online recommendation task (downstream task).

Recommendation Systems

Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model

no code implementations12 Aug 2022 Xiang-Rong Sheng, Jingyue Gao, Yueyao Cheng, Siran Yang, Shuguang Han, Hongbo Deng, Yuning Jiang, Jian Xu, Bo Zheng

It can be attributed to the calibration ability of the pointwise loss since the prediction can be viewed as the click probability.

Click-Through Rate Prediction

Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction

no code implementations21 Dec 2021 Kailun Wu, Zhangming Chan, Weijie Bian, Lejian Ren, Shiming Xiang, Shuguang Han, Hongbo Deng, Bo Zheng

We further show that such a process is equivalent to adding an adversarial perturbation to the model input, and thereby name our proposed approach as an the Adversarial Gradient Driven Exploration (AGE).

Click-Through Rate Prediction Recommendation Systems

Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling

1 code implementation6 Dec 2020 Jia-Qi Yang, Xiang Li, Shuguang Han, Tao Zhuang, De-Chuan Zhan, Xiaoyi Zeng, Bin Tong

To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution.

Learning-to-Rank with BERT in TF-Ranking

no code implementations17 Apr 2020 Shuguang Han, Xuanhui Wang, Mike Bendersky, Marc Najork

This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance.

Document Ranking Learning-To-Rank +2

Deep Keyphrase Generation

4 code implementations ACL 2017 Rui Meng, Sanqiang Zhao, Shuguang Han, Daqing He, Peter Brusilovsky, Yu Chi

Keyphrase provides highly-condensed information that can be effectively used for understanding, organizing and retrieving text content.

Keyphrase Extraction Keyphrase Generation

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