60 citations · 101 across the 14 of their papers we have counts for
15 papers
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving
Guantian Zheng, Lidong Yu, Tianyu Gao +3
End-to-end driving policies can achieve strong task performance while offering limited control over their driving style. Driving style describes how a vehicle accelerates, brakes,…
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
Yunfeng Zhao, Xu Yan, Xiaoqiang Gui +6
Conversion rate (CVR) prediction is an essential task for large-scale e-commerce platforms. However, refund behaviors frequently occur after conversion in online shopping systems,…
Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach
Zhangming Chan, Yu Zhang, Shuguang Han +8
Conversion rate (CVR) prediction is one of the core components in online recommender systems, and various approaches have been proposed to obtain accurate and well-calibrated CVR e…
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao
Jingyue Gao, Shuguang Han, Han Zhu +4
Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user f…
COPR: Consistency-Oriented Pre-Ranking for Online Advertising
Zhishan Zhao, Jingyue Gao, Yu Zhang +8
Cascading architecture has been widely adopted in large-scale advertising systems to balance efficiency and effectiveness. In this architecture, the pre-ranking model is expected t…
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
Zhao-Yu Zhang, Xiang-Rong Sheng, Yujing Zhang +4
Deep learning techniques have been applied widely in industrial recommendation systems. However, far less attention has been paid to the overfitting problem of models in recommenda…
KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging
Yujing Zhang, Zhangming Chan, Shuhao Xu +4
An industrial recommender system generally presents a hybrid list that contains results from multiple subsystems. In practice, each subsystem is optimized with its own feedback dat…
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
Xiang-Rong Sheng, Jingyue Gao, Yueyao Cheng +6
Despite the development of ranking optimization techniques, pointwise loss remains the dominating approach for click-through rate prediction. It can be attributed to the calibratio…
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
Kailun Wu, Zhangming Chan, Weijie Bian +5
Exploration-Exploitation (E{\&}E) algorithms are commonly adopted to deal with the feedback-loop issue in large-scale online recommender systems. Most of existing studies believe t…
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
Jia-Qi Yang, Xiang Li, Shuguang Han +4
Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner t…
RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble
Michael Bendersky, Honglei Zhuang, Ji Ma +3
In this paper, we report the results of our participation in the TREC-COVID challenge. To meet the challenge of building a search engine for rapidly evolving biomedical collection,…
Learning-to-Rank with BERT in TF-Ranking
Shuguang Han, Xuanhui Wang, Mike Bendersky +1
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…
Deep Keyphrase Generation
Rui Meng, Sanqiang Zhao, Shuguang Han +3
Keyphrase provides highly-condensed information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many w…
Benchmarking the Privacy-Preserving People Search
Shuguang Han, Daqing He, Zhen Yue
People search is an important topic in information retrieval. Many previous studies on this topic employed social networks to boost search performance by incorporating either local…
Automatic Detection of Search Tactic in Individual Information Seeking: A Hidden Markov Model Approach
Shuguang Han, Zhen Yue, Daqing He
Information seeking process is an important topic in information seeking behavior research. Both qualitative and empirical methods have been adopted in analyzing information seekin…