Search Results for author: Xiang-Rong Sheng

Found 9 papers, 3 papers with code

Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling

1 code implementation29 Apr 2021 Siyu Gu, Xiang-Rong Sheng, Ying Fan, Guorui Zhou, Xiaoqiang Zhu

If conversion happens outside the waiting window, this sample will be duplicated and ingested into the training pipeline with a positive label.

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

CAN: Feature Co-Action for Click-Through Rate Prediction

no code implementations11 Nov 2020 Weijie Bian, Kailun Wu, Lejian Ren, Qi Pi, Yujing Zhang, Can Xiao, Xiang-Rong Sheng, Yong-Nan Zhu, Zhangming Chan, Na Mou, Xinchen Luo, Shiming Xiang, Guorui Zhou, Xiaoqiang Zhu, Hongbo Deng

For example, a simple attempt to learn the combination of feature A and feature B <A, B> as the explicit cartesian product representation of new features can outperform previous implicit feature interaction models including factorization machine (FM)-based models and their variations.

Click-Through Rate Prediction

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

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.

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

Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction

no code implementations14 Dec 2023 Xiaoqiang Gui, Yueyao Cheng, Xiang-Rong Sheng, Yunfeng Zhao, Guoxian Yu, Shuguang Han, Yuning Jiang, Jian Xu, Bo Zheng

A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving.

Click-Through Rate Prediction

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