623 citations · 1.9k across the 48 of their papers we have counts for
17 papers · 1 filter
Effective and Efficient Query-aware Snippet Extraction for Web Search
Jingwei Yi, Fangzhao Wu, Chuhan Wu +4
Query-aware webpage snippet extraction is widely used in search engines to help users better understand the content of the returned webpages before clicking. Although important, it…
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
Sungwon Han, Sungwon Park, Fangzhao Wu +4
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased representation from decentralized and heterogeneous local data. It employs a two-s…
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Tao Qi, Fangzhao Wu, Chuhan Wu +5
Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving w…
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus o…
FUM: Fine-grained and Fast User Modeling for News Recommendation
Tao Qi, Fangzhao Wu, Chuhan Wu +1
User modeling is important for news recommendation. Existing methods usually first encode user's clicked news into news embeddings independently and then aggregate them into user e…
News Recommendation with Candidate-aware User Modeling
Tao Qi, Fangzhao Wu, Chuhan Wu +1
News recommendation aims to match news with personalized user interest. Existing methods for news recommendation usually model user interest from historical clicked news without th…
ProFairRec: Provider Fairness-aware News Recommendation
Tao Qi, Fangzhao Wu, Chuhan Wu +5
News recommendation aims to help online news platform users find their preferred news articles. Existing news recommendation methods usually learn models from historical user behav…
Unified and Effective Ensemble Knowledge Distillation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Ensemble knowledge distillation can extract knowledge from multiple teacher models and encode it into a single student model. Many existing methods learn and distill the student mo…
FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Single-tower models are widely used in the ranking stage of news recommendation to accurately rank candidate news according to their fine-grained relatedness with user interest ind…
End-to-end Learnable Diversity-aware News Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Diversity is an important factor in providing high-quality personalized news recommendations. However, most existing news recommendation methods only aim to optimize recommendation…
Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Adversarial learning is a widely used technique in fair representation learning to remove the biases on sensitive attributes from data representations. It usually requires to incor…
NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Effectively finetuning pretrained language models (PLMs) is critical for their success in downstream tasks. However, PLMs may have risks in overfitting the pretraining tasks and da…
Are Big Recommendation Models Fair to Cold Users?
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Big models are widely used by online recommender systems to boost recommendation performance. They are usually learned on historical user behavior data to infer user interest and p…
Quality-aware News Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
News recommendation is a core technique used by many online news platforms. Recommending high-quality news to users is important for keeping good user experiences and news platform…
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Federated learning (FL) is a feasible technique to learn personalized recommendation models from decentralized user data. Unfortunately, federated recommender systems are vulnerabl…
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices
Ruixuan Liu, Fangzhao Wu, Chuhan Wu +4
Federated learning (FL) is an important paradigm for training global models from decentralized data in a privacy-preserving way. Existing FL methods usually assume the global model…
Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction
Chuhan Wu, Fangzhao Wu, Tao Qi +4
Vertical federated learning (VFL) aims to train models from cross-silo data with different feature spaces stored on different platforms. Existing VFL methods usually assume all dat…