79 citations · 160 across the 10 of their papers we have counts for
6 papers · 1 filter
MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios
Xiaofeng Pan, Ming Li, Jing Zhang +5
Different from large-scale platforms such as Taobao and Amazon, CVR modeling in small-scale recommendation scenarios is more challenging due to the severe Data Distribution Fluctua…
MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction
Xiaofeng Pan, Yibin Shen, Jing Zhang +5
Promotions are becoming more important and prevalent in e-commerce to attract customers and boost sales, leading to frequent changes of occasions, which drives users to behave diff…
SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios
Qijie Shen, Wanjie Tao, Jing Zhang +3
The travel marketing platform of Alibaba serves an indispensable role for hundreds of different travel scenarios from Fliggy, Taobao, Alipay apps, etc. To provide personalized reco…
Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction
Hong Wen, Jing Zhang, Fuyu Lv +3
Conversion Rate (\emph{CVR}) prediction in modern industrial e-commerce platforms is becoming increasingly important, which directly contributes to the final revenue. In order to a…
Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
Hong Wen, Jing Zhang, Yuan Wang +4
Recommender system, as an essential part of modern e-commerce, consists of two fundamental modules, namely Click-Through Rate (CTR) and Conversion Rate (CVR) prediction. While CVR…
Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System
Hong Wen, Jing Zhang, Quan Lin +2
Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms. Generally speaking, two essential modules named "Click-Through Rate P…