1.5k citations · 6.3k across the 35 of their papers we have counts for
18 papers · 1 filter
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…
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…
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…
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…
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…
Cross-domain novelty seeking trait mining for sequential recommendation
Fuzhen Zhuang, Yingmin Zhou, Fuzheng Zhang +3
Transfer learning has attracted a large amount of interest and research in last decades, and some efforts have been made to build more precise recommendation systems. Most previous…
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
Hongwei Wang, Fuzheng Zhang, Jialin Wang +4
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve…