80 citations · 155 across the 24 of their papers we have counts for
31 papers
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…