79 citations · 160 across the 11 of their papers we have counts for
11 papers
Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction
Peilin Chen, Hong Wen, Jing Zhang +6
Online travel platforms (OTPs), e.g., Ctrip.com or Fliggy.com, can effectively provide travel-related products or services to users. In this paper, we focus on the multi-scenario c…
Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search
Qijie Shen, Hong Wen, Jing Zhang +1
Estimating Click-Through Rate (CTR) is a vital yet challenging task in personalized product search. However, existing CTR methods still struggle in the product search settings due…
Re-weighting Negative Samples for Model-Agnostic Matching
Jiazhen Lou, Hong Wen, Fuyu Lv +3
Recommender Systems (RS), as an efficient tool to discover users' interested items from a very large corpus, has attracted more and more attention from academia and industry. As th…
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
Qijie Shen, Hong Wen, Wanjie Tao +4
In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of p…
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…
Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning
Wenhao Zhang, Wentian Bao, Xiao-Yang Liu +4
Post-click conversion rate (CVR) estimation is a critical task in e-commerce recommender systems. This task is deemed quite challenging under the industrial setting with two major…
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…