79 citations · 160 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…