623 citations · 1.9k across the 48 of their papers we have counts for
13 papers · 1 filter
Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation
Yang Yu, Fangzhao Wu, Chuhan Wu +2
News recommendation is a widely adopted technique to provide personalized news feeds for the user. Recently, pre-trained language models (PLMs) have demonstrated the great capabili…
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…
Fastformer: Additive Attention Can Be All You Need
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Transformer is a powerful model for text understanding. However, it is inefficient due to its quadratic complexity to input sequence length. Although there are many methods on Tran…
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,…
Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer
Chuhan Wu, Fangzhao Wu, Tao Qi +4
Transformer has achieved great success in NLP. However, the quadratic complexity of the self-attention mechanism in Transformer makes it inefficient in handling long sequences. Man…
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation
Jingwei Yi, Fangzhao Wu, Chuhan Wu +3
News recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users' historical news click behavior data, w…
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…
FedKD: Communication Efficient Federated Learning via Knowledge Distillation
Chuhan Wu, Fangzhao Wu, Lingjuan Lyu +2
Federated learning is widely used to learn intelligent models from decentralized data. In federated learning, clients need to communicate their local model updates in each iteratio…
Personalized News Recommendation: Methods and Challenges
Chuhan Wu, Fangzhao Wu, Yongfeng Huang +1
Personalized news recommendation is an important technique to help users find their interested news information and alleviate their information overload. It has been extensively st…
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…
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application
Chuhan Wu, Fangzhao Wu, Yang Yu +3
Pre-trained language models (PLMs) like BERT have made great progress in NLP. News articles usually contain rich textual information, and PLMs have the potentials to enhance news t…
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…