14 citations · 40 across the 10 of their papers we have counts for
10 papers
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…
One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers
Chuhan Wu, Fangzhao Wu, Yongfeng Huang
Pre-trained language models (PLMs) achieve great success in NLP. However, their huge model sizes hinder their applications in many practical systems. Knowledge distillation is a po…
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…
Rethinking InfoNCE: How Many Negative Samples Do You Need?
Chuhan Wu, Fangzhao Wu, Yongfeng Huang
InfoNCE loss is a widely used loss function for contrastive model training. It aims to estimate the mutual information between a pair of variables by discriminating between each po…