b'Chuhan Wu'

research

∙ 06/26/2023

Contrastive Multi-view Framework for Customer Lifetime Value Prediction

Accurate customer lifetime value (LTV) prediction can help service provi...

0 Chuhan Wu, et al. ∙

research

∙ 03/15/2023

DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision

Algorithmic fairness has become an important machine learning problem, e...

0 Sungwon Han, et al. ∙

research

∙ 10/17/2022

Effective and Efficient Query-aware Snippet Extraction for Web Search

Query-aware webpage snippet extraction is widely used in search engines ...

0 Jingwei Yi, et al. ∙

research

∙ 07/19/2022

FedX: Unsupervised Federated Learning with Cross Knowledge Distillation

This paper presents FedX, an unsupervised federated learning framework. ...

0 Sungwon Han, et al. ∙

research

∙ 06/07/2022

FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

Vertical federated learning (VFL) is a privacy-preserving machine learni...

0 Tao Qi, et al. ∙

research

∙ 04/21/2022

FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation

Contrastive learning is widely used for recommendation model learning, w...

0 Chuhan Wu, et al. ∙

research

∙ 04/18/2022

PrivateRec: Differentially Private Training and Serving for Federated News Recommendation

Privacy protection is an essential issue in personalized news recommenda...

0 Ruixuan Liu, et al. ∙

research

∙ 04/10/2022

FUM: Fine-grained and Fast User Modeling for News Recommendation

User modeling is important for news recommendation. Existing methods usu...

0 Tao Qi, et al. ∙

research

∙ 04/10/2022

News Recommendation with Candidate-aware User Modeling

News recommendation aims to match news with personalized user interest. ...

0 Tao Qi, et al. ∙

research

∙ 04/10/2022

ProFairRec: Provider Fairness-aware News Recommendation

News recommendation aims to help online news platform users find their p...

0 Tao Qi, et al. ∙

research

∙ 04/01/2022

Unified and Effective Ensemble Knowledge Distillation

Ensemble knowledge distillation can extract knowledge from multiple teac...

0 Chuhan Wu, et al. ∙

research

∙ 04/01/2022

FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation

Single-tower models are widely used in the ranking stage of news recomme...

0 Chuhan Wu, et al. ∙

research

∙ 04/01/2022

End-to-end Learnable Diversity-aware News Recommendation

Diversity is an important factor in providing high-quality personalized ...

0 Chuhan Wu, et al. ∙

research

∙ 04/01/2022

Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder

Adversarial learning is a widely used technique in fair representation l...

0 Chuhan Wu, et al. ∙

research

∙ 02/28/2022

Are Big Recommendation Models Fair to Cold Users?

Big models are widely used by online recommender systems to boost recomm...

0 Chuhan Wu, et al. ∙

research

∙ 02/28/2022

Quality-aware News Recommendation

News recommendation is a core technique used by many online news platfor...

0 Chuhan Wu, et al. ∙

research

∙ 02/24/2022

NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better

Effectively finetuning pretrained language models (PLMs) is critical for...

0 Chuhan Wu, et al. ∙

research

∙ 02/16/2022

No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices

Federated learning (FL) is an important paradigm for training global mod...

0 Ruixuan Liu, et al. ∙

research

∙ 02/10/2022

Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction

Vertical federated learning (VFL) aims to train models from cross-silo d...

0 Chuhan Wu, et al. ∙

research

∙ 02/10/2022

FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling

Federated learning (FL) is a feasible technique to learn personalized re...

0 Chuhan Wu, et al. ∙

research

∙ 12/02/2021

Tiny-NewsRec: Efficient and Effective PLM-based News Recommendation

Personalized news recommendation has been widely adopted to improve user...

0 Yang Yu, et al. ∙

research

∙ 09/12/2021

Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation

News recommendation is critical for personalized news access. Most exist...

0 Jingwei Yi, et al. ∙

research

∙ 09/11/2021

Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving

News recommendation is important for personalized online news services. ...

0 Tao Qi, et al. ∙

research

∙ 09/03/2021

UserBERT: Contrastive User Model Pre-training

User modeling is critical for personalized web applications. Existing us...

0 Chuhan Wu, et al. ∙

research

∙ 08/30/2021

FedKD: Communication Efficient Federated Learning via Knowledge Distillation

Federated learning is widely used to learn intelligent models from decen...

0 Chuhan Wu, et al. ∙

research

∙ 08/20/2021

Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer

Transformer has achieved great success in NLP. However, the quadratic co...

0 Chuhan Wu, et al. ∙

research

∙ 08/20/2021

Fastformer: Additive Attention Can Be All You Need

Transformer is a powerful model for text understanding. However, it is i...

0 Chuhan Wu, et al. ∙

research

∙ 08/20/2021

Is News Recommendation a Sequential Recommendation Task?

News recommendation is often modeled as a sequential recommendation task...

0 Chuhan Wu, et al. ∙

research

∙ 06/16/2021

Personalized News Recommendation: A Survey

Personalized news recommendation is an important technique to help users...

0 Chuhan Wu, et al. ∙

research

∙ 06/11/2021

DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning

News recommendation is important for improving news reading experience o...

0 Chuhan Wu, et al. ∙

research

∙ 06/08/2021

HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation

User interest modeling is critical for personalized news recommendation....

0 Tao Qi, et al. ∙

research

∙ 06/02/2021

PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity

Personalized news recommendation methods are widely used in online news ...

0 Tao Qi, et al. ∙

research

∙ 06/02/2021

Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling

Transformer is important for text modeling. However, it has difficulty i...

0 Chuhan Wu, et al. ∙

research

∙ 06/02/2021

One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers

Pre-trained language models (PLMs) achieve great success in NLP. However...

0 Chuhan Wu, et al. ∙

research

∙ 05/27/2021

Rethinking InfoNCE: How Many Negative Samples Do You Need?

InfoNCE loss is a widely used loss function for contrastive model traini...

0 Chuhan Wu, et al. ∙

research

∙ 04/20/2021

Personalized News Recommendation with Knowledge-aware Interactive Matching

The most important task in personalized news recommendation is accurate ...

0 Tao Qi, et al. ∙

research

∙ 04/15/2021

Empowering News Recommendation with Pre-trained Language Models

Personalized news recommendation is an essential technique for online ne...

0 Chuhan Wu, et al. ∙

research

∙ 04/15/2021

MM-Rec: Multimodal News Recommendation

Accurate news representation is critical for news recommendation. Most o...

0 Chuhan Wu, et al. ∙

research

∙ 04/15/2021

Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation

Recall and ranking are two critical steps in personalized news recommend...

0 Chuhan Wu, et al. ∙

research

∙ 04/15/2021

DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation

News recommendation is critical for personalized news access. Existing n...

0 Jingwei Yi, et al. ∙

research

∙ 02/09/2021

FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

Graph neural network (GNN) is widely used for recommendation to model hi...

0 Chuhan Wu, et al. ∙

research

∙ 02/09/2021

FeedRec: News Feed Recommendation with Various User Feedbacks

Personalized news recommendation techniques are widely adopted by many o...

0 Chuhan Wu, et al. ∙

research

∙ 02/09/2021

NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application

Pre-trained language models (PLMs) like BERT have made great progress in...

14 Chuhan Wu, et al. ∙

research

∙ 01/12/2021

Neural News Recommendation with Negative Feedback

News recommendation is important for online news services. Precise user ...

0 Chuhan Wu, et al. ∙

research

∙ 10/14/2020

DA-Transformer: Distance-aware Transformer

Transformer has achieved great success in the NLP field by composing var...

0 Chuhan Wu, et al. ∙

research

∙ 10/08/2020

Improving Attention Mechanism with Query-Value Interaction

Attention mechanism has played critical roles in various state-of-the-ar...

0 Chuhan Wu, et al. ∙

research

∙ 10/04/2020

PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision

User modeling is critical for many personalized web services. Many exist...

0 Chuhan Wu, et al. ∙

research

∙ 07/23/2020

FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior Data

Native ad is a popular type of online advertisement which has similar fo...

0 Chuhan Wu, et al. ∙

research

∙ 06/30/2020

Fairness-aware News Recommendation with Decomposed Adversarial Learning

News recommendation is important for online news services. Most news rec...

0 Chuhan Wu, et al. ∙

research

∙ 03/31/2020

Graph Enhanced Representation Learning for News Recommendation

With the explosion of online news, personalized news recommendation beco...

0 Suyu Ge, et al. ∙

Chuhan Wu

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