1.5k citations · 6.3k across the 35 of their papers we have counts for
35 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…
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…
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…
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…
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…
FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning
Suyu Ge, Fangzhao Wu, Chuhan Wu +3
Medical named entity recognition (NER) has wide applications in intelligent healthcare. Sufficient labeled data is critical for training accurate medical NER model. However, the la…
Privacy-Preserving News Recommendation Model Learning
Tao Qi, Fangzhao Wu, Chuhan Wu +2
News recommendation aims to display news articles to users based on their personal interest. Existing news recommendation methods rely on centralized storage of user behavior data…
A Survey on Knowledge Graph-Based Recommender Systems
Qingyu Guo, Fuzhen Zhuang, Chuan Qin +4
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although…
Neural News Recommendation with Attentive Multi-View Learning
Chuhan Wu, Fangzhao Wu, Mingxiao An +3
Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning i…
NPA: Neural News Recommendation with Personalized Attention
Chuhan Wu, Fangzhao Wu, Mingxiao An +3
News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may…
Personalized Multimedia Item and Key Frame Recommendation
Le Wu, Lei Chen, Yonghui Yang +4
When recommending or advertising items to users, an emerging trend is to present each multimedia item with a key frame image (e.g., the poster of a movie). As each multimedia item…
Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors
Xianchen Wang, Hongtao Liu, Peiyi Wang +4
Text reviews can provide rich useful semantic information for modeling users and items, which can benefit rating prediction in recommendation. Different words and reviews may have…
NRPA: Neural Recommendation with Personalized Attention
Hongtao Liu, Fangzhao Wu, Wenjun Wang +4
Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, differe…
Transcribing Content from Structural Images with Spotlight Mechanism
Yu Yin, Zhenya Huang, Enhong Chen +4
Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal str…
Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation
Fangzhao Wu, Junxin Liu, Chuhan Wu +2
Chinese named entity recognition (CNER) is an important task in Chinese natural language processing field. However, CNER is very challenging since Chinese entity names are highly c…
Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization
Junxin Liu, Fangzhao Wu, Chuhan Wu +2
Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unl…
Knowledge Graph Convolutional Networks for Recommender Systems
Hongwei Wang, Miao Zhao, Xing Xie +2
To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and desi…
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
Hongwei Wang, Fuzheng Zhang, Miao Zhao +3
Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to a…
Session-based Recommendation with Graph Neural Networks
Shu Wu, Yuyuan Tang, Yanqiao Zhu +3
The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representatio…
Social media and mobility landscape: uncovering spatial patterns of urban human mobility with multi source data
Yilan Cui, Xing Xie, Yi Liu
In this paper, we present a three-step methodological framework, including location identification, bias modification, and out-of-sample validation, so as to promote human mobility…