315 citations · 1.5k across the 58 of their papers we have counts for
20 papers · 1 filter
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…
Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving
Tao Qi, Fangzhao Wu, Chuhan Wu +2
News recommendation is important for personalized online news services. Most existing news recommendation methods rely on centrally stored user behavior data to both train models o…
UserBERT: Contrastive User Model Pre-training
Chuhan Wu, Fangzhao Wu, Yang Yu +3
User modeling is critical for personalized web applications. Existing user modeling methods usually train user models from user behaviors with task-specific labeled data. However,…
Is News Recommendation a Sequential Recommendation Task?
Chuhan Wu, Fangzhao Wu, Tao Qi +1
News recommendation is often modeled as a sequential recommendation task, which assumes that there are rich short-term dependencies over historical clicked news. However, in news r…
DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning
Chuhan Wu, Fangzhao Wu, Yongfeng Huang
News recommendation is important for improving news reading experience of users. Users' news click behaviors are widely used for inferring user interests and predicting future clic…
PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity
Tao Qi, Fangzhao Wu, Chuhan Wu +1
Personalized news recommendation methods are widely used in online news services. These methods usually recommend news based on the matching between news content and user interest…
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation
Tao Qi, Fangzhao Wu, Chuhan Wu +4
User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previou…
Personalized News Recommendation with Knowledge-aware Interactive Matching
Tao Qi, Fangzhao Wu, Chuhan Wu +1
The most important task in personalized news recommendation is accurate matching between candidate news and user interest. Most of existing news recommendation methods model candid…
Empowering News Recommendation with Pre-trained Language Models
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Personalized news recommendation is an essential technique for online news services. News articles usually contain rich textual content, and accurate news modeling is important for…
MM-Rec: Multimodal News Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Accurate news representation is critical for news recommendation. Most of existing news representation methods learn news representations only from news texts while ignore the visu…
Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Recall and ranking are two critical steps in personalized news recommendation. Most existing news recommender systems conduct personalized news recall and ranking separately with d…
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
Chuhan Wu, Fangzhao Wu, Yang Cao +2
Graph neural network (GNN) is widely used for recommendation to model high-order interactions between users and items. Existing GNN-based recommendation methods rely on centralized…
FeedRec: News Feed Recommendation with Various User Feedbacks
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Accurate user interest modeling is important for news recommendation. Most existing methods for news recommendation rely on implicit feedbacks like click for inferring user interes…
Neural News Recommendation with Negative Feedback
Chuhan Wu, Fangzhao Wu, Yongfeng Huang +1
News recommendation is important for online news services. Precise user interest modeling is critical for personalized news recommendation. Existing news recommendation methods usu…
PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision
Chuhan Wu, Fangzhao Wu, Tao Qi +3
User modeling is critical for many personalized web services. Many existing methods model users based on their behaviors and the labeled data of target tasks. However, these method…
FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior Data
Chuhan Wu, Fangzhao Wu, Tao Di +2
Native ad is a popular type of online advertisement which has similar forms with the native content displayed on websites. Native ad CTR prediction is useful for improving user exp…
FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning
Chuhan Wu, Fangzhao Wu, Xiting Wang +2
News recommendation is important for online news services. Existing news recommendation models are usually learned from users' news click behaviors. Usually the behaviors of users…
Graph Enhanced Representation Learning for News Recommendation
Suyu Ge, Chuhan Wu, Fangzhao Wu +2
With the explosion of online news, personalized news recommendation becomes increasingly important for online news platforms to help their users find interesting information. Exist…
Privacy-Preserving News Recommendation Model Learning
Tao Qi, Fangzhao Wu, Chuhan Wu +2
News recommendation aims to display news articles to users based on their personal interest. Existing news recommendation methods rely on centralized storage of user behavior data…
NPA: Neural News Recommendation with Personalized Attention
Chuhan Wu, Fangzhao Wu, Mingxiao An +3
News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may…