activity
20172022
most citedNPA: Neural News Recommendation with Personalized Attention

315 citations · 623 across the 37 of their papers we have counts for

collaborators
Showing 2020Show all

9 papers · 1 filter

cs.CL2020

Improving Attention Mechanism with Query-Value Interaction

Chuhan Wu, Fangzhao Wu, Tao Qi +1

Attention mechanism has played critical roles in various state-of-the-art NLP models such as Transformer and BERT. It can be formulated as a ternary function that maps the input qu…

cs.IR2020★ 10 cited

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…

cs.CL2020

DA-Transformer: Distance-aware Transformer

Chuhan Wu, Fangzhao Wu, Yongfeng Huang

Transformer has achieved great success in the NLP field by composing various advanced models like BERT and GPT. However, Transformer and its existing variants may not be optimal in…

cs.IR2020★ 5 cited

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…

cs.CL2020★ 8 cited

Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph

Zhongliang Yang, Baitao Gong, Yamin Li +3

Most of the existing text generative steganographic methods are based on coding the conditional probability distribution of each word during the generation process, and then select…

cs.IR2020

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…