1k citations · 2.1k across the 21 of their papers we have counts for
32 papers
Multi-Stage Network Embedding for Exploring Heterogeneous Edges
Hong Huang, Yu Song, Fanghua Ye +3
The relationships between objects in a network are typically diverse and complex, leading to the heterogeneous edges with different semantic information. In this paper, we focus on…
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…
DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation
Jingwei Yi, Fangzhao Wu, Chuhan Wu +3
News recommendation is critical for personalized news access. Existing news recommendation methods usually infer users' personal interest based on their historical clicked news, an…
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…
Fake News Detection through Graph Comment Advanced Learning
Hao Liao, Qixin Liu, Kai Shu +1
Disinformation has long been regarded as a severe social problem, where fake news is one of the most representative issues. What is worse, today's highly developed social media mak…
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…