24 citations · 61 across the 12 of their papers we have counts for
12 papers
MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising
Zhen Gong, Lvyin Niu, Yang Zhao +9
Online bidding and auction are crucial aspects of the online advertising industry. Conventionally, there is only one slot for ad display and most current studies focus on it. Nowad…
Multi-Scenario Ranking with Adaptive Feature Learning
Yu Tian, Bofang Li, Si Chen +6
Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mi…
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…
Sustainable Online Reinforcement Learning for Auto-bidding
Zhiyu Mou, Yusen Huo, Rongquan Bai +4
Recently, auto-bidding technique has become an essential tool to increase the revenue of advertisers. Facing the complex and ever-changing bidding environments in the real-world ad…
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…
Visual Encoding and Debiasing for CTR Prediction
Si Chen, Chen Lin, Wanxian Guan +7
Extracting expressive visual features is crucial for accurate Click-Through-Rate (CTR) prediction in visual search advertising systems. Current commercial systems use off-the-shelf…
GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Model
Wenbo Su, Yuanxing Zhang, Yufeng Cai +9
High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly dep…
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction
Bencheng Yan, Pengjie Wang, Kai Zhang +4
In many web applications, deep learning-based CTR prediction models (deep CTR models for short) are widely adopted. Traditional deep CTR models learn patterns in a static manner, i…
Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction
Yu Chen, Jiaqi Jin, Hui Zhao +4
Alleviating the delayed feedback problem is of crucial importance for the conversion rate(CVR) prediction in online advertising. Previous delayed feedback modeling methods using an…
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Ningyu Zhang, Mosha Chen, Zhen Bi +20
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical langua…