Search Results for author: Hong Wen

Found 12 papers, 2 papers with code

Modeling User Viewing Flow Using Large Language Models for Article Recommendation

no code implementations12 Nov 2023 Zhenghao Liu, Zulong Chen, Moufeng Zhang, Shaoyang Duan, Hong Wen, Liangyue Li, Nan Li, Yu Gu, Ge Yu

This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles.

Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction

no code implementations16 Apr 2023 Peilin Chen, Hong Wen, Jing Zhang, Fuyu Lv, Zhao Li, Qijie Shen, Wanjie Tao, Ying Zhou, Chao Zhang

Online travel platforms (OTPs), e. g., Ctrip. com or Fliggy. com, can effectively provide travel-related products or services to users.

Click-Through Rate Prediction

Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search

no code implementations4 Apr 2023 Qijie Shen, Hong Wen, Jing Zhang, Qi Rao

Specifically, SIE is proposed to extract user's short-term interests by integrating three fundamental interests encoders within it namely query-dependent, target-dependent and causal-dependent interest encoder, respectively, followed by delivering the resultant representation to the module LIE, where it can effectively capture user long-term interests by devising an attention mechanism with respect to the short-term interests from SIE module.

Click-Through Rate Prediction Disentanglement

Re-weighting Negative Samples for Model-Agnostic Matching

no code implementations6 Jul 2022 Jiazhen Lou, Hong Wen, Fuyu Lv, Jing Zhang, Tengfei Yuan, Zhao Li

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.

Multi-Task Learning Recommendation Systems

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

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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