Search Results for author: Hong Wen

Found 8 papers, 1 papers with code

Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation

1 code implementation5 Feb 2022 Qijie Shen, Hong Wen, Wanjie Tao, Jing Zhang, Fuyu Lv, Zulong Chen, Zhao Li

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 platforms.

Click-Through Rate Prediction

Scenario Adaptive Mixture-of-Experts for Promotion-Aware Click-Through Rate Prediction

no code implementations27 Dec 2021 Xiaofeng Pan, Yibin Shen, Jing Zhang, Keren Yu, Hong Wen, Shui Liu, Chengjun Mao, Bo Cao

To tackle the distribution uncertainty, a set of scenario signals are elaborately devised from a perspective of time series prediction and fed into the FGN, whose output is concatenated with feature representation from each expert to learn the attention.

Click-Through Rate Prediction Recommendation Systems +2

MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios

no code implementations27 Dec 2021 Xiaofeng Pan, Ming Li, Jing Zhang, Keren Yu, Luping Wang, Hong Wen, Chengjun Mao, Bo Cao

At last, we develop an Ensemble Prediction Network (EPN) which incorporates the output of FRN and DMN to make the final CVR prediction.

Meta-Learning

Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction

no code implementations20 Apr 2021 Hong Wen, Jing Zhang, Fuyu Lv, Wentian Bao, Tianyi Wang, Zulong Chen

Motivated by this observation, we propose a novel \emph{CVR} prediction method by Hierarchically Modeling both Micro and Macro behaviors ($HM^3$).

Multi-Task Learning Selection bias

Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction

no code implementations15 Oct 2019 Hong Wen, Jing Zhang, Yu-An Wang, Fuyu Lv, Wentian Bao, Quan Lin, Keping Yang

Although existing methods, typically built on the user sequential behavior path ``impression$\to$click$\to$purchase'', is effective for dealing with SSB issue, they still struggle to address the DS issue due to rare purchase training samples.

Click-Through Rate Prediction Multi-Task Learning +2

Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System

no code implementations24 May 2018 Hong Wen, Jing Zhang, Quan Lin, Keping Yang, Pipei Huang

The deep cascade structure and the combination rule enable the proposed \textit{ldcTree} to have a stronger distributed feature representation ability.

Click-Through Rate Prediction Ensemble Learning

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