1.2k citations · 3.2k across the 31 of their papers we have counts for
31 papers
Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection
Beizhe Hu, Qiang Sheng, Juan Cao +4
Fake news detection has been a critical task for maintaining the health of the online news ecosystem. However, very few existing works consider the temporal shift issue caused by t…
Modeling Dual Period-Varying Preferences for Takeaway Recommendation
Yuting Zhang, Yiqing Wu, Ran Le +7
Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from trad…
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning
Xin Qin, Jindong Wang, Shuo Ma +4
Human activity recognition (HAR) is a time series classification task that focuses on identifying the motion patterns from human sensor readings. Adequate data is essential but a m…
Attacking Pre-trained Recommendation
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained…
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer
Qiong Nan, Danding Wang, Yongchun Zhu +4
Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple…
Customized Conversational Recommender Systems
Shuokai Li, Yongchun Zhu, Ruobing Xie +5
Conversational recommender systems (CRS) aim to capture user's current intentions and provide recommendations through real-time multi-turn conversational interactions. As a human-m…
Memory-Guided Multi-View Multi-Domain Fake News Detection
Yongchun Zhu, Qiang Sheng, Juan Cao +5
The wide spread of fake news is increasingly threatening both individuals and society. Great efforts have been made for automatic fake news detection on a single domain (e.g., poli…
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Zhenwei Tang, Shichao Pei, Zhao Zhang +4
Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given…
Personalized Prompt for Sequential Recommendation
Yiqing Wu, Ruobing Xie, Yongchun Zhu +4
Pre-training models have shown their power in sequential recommendation. Recently, prompt has been widely explored and verified for tuning in NLP pre-training, which could help to…
Selective Fairness in Recommendation via Prompts
Yiqing Wu, Ruobing Xie, Yongchun Zhu +5
Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and user…
User-Centric Conversational Recommendation with Multi-Aspect User Modeling
Shuokai Li, Ruobing Xie, Yongchun Zhu +3
Conversational recommender systems (CRS) aim to provide highquality recommendations in conversations. However, most conventional CRS models mainly focus on the dialogue understandi…
Generalizing to the Future: Mitigating Entity Bias in Fake News Detection
Yongchun Zhu, Qiang Sheng, Juan Cao +3
The wide dissemination of fake news is increasingly threatening both individuals and society. Fake news detection aims to train a model on the past news and detect fake news of the…
Multi-view Multi-behavior Contrastive Learning in Recommendation
Yiqing Wu, Ruobing Xie, Yongchun Zhu +6
Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior's performance. We argue that MBR models should: (1) model the coarse-…
Zoom Out and Observe: News Environment Perception for Fake News Detection
Qiang Sheng, Juan Cao, Xueyao Zhang +3
Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones, existing methods observe the language…
Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection
Yongchun Zhu, Dongbo Xi, Bowen Song +4
With the explosive growth of the e-commerce industry, detecting online transaction fraud in real-world applications has become increasingly important to the development of e-commer…
Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources
Yongchun Zhu, Fuzhen Zhuang, Deqing Wang
While Unsupervised Domain Adaptation (UDA) algorithms, i.e., there are only labeled data from source domains, have been actively studied in recent years, most algorithms and theore…
Multi-Representation Adaptation Network for Cross-domain Image Classification
Yongchun Zhu, Fuzhen Zhuang, Jindong Wang +4
In image classification, it is often expensive and time-consuming to acquire sufficient labels. To solve this problem, domain adaptation often provides an attractive option given a…
MDFEND: Multi-domain Fake News Detection
Qiong Nan, Juan Cao, Yongchun Zhu +2
Fake news spread widely on social media in various domains, which lead to real-world threats in many aspects like politics, disasters, and finance. Most existing approaches focus o…
Neural Hierarchical Factorization Machines for User's Event Sequence Analysis
Dongbo Xi, Fuzhen Zhuang, Bowen Song +6
Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance. However, existing popular…
Personalized Transfer of User Preferences for Cross-domain Recommendation
Yongchun Zhu, Zhenwei Tang, Yudan Liu +5
Cold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-st…
Deep Subdomain Adaptation Network for Image Classification
Yongchun Zhu, Fuzhen Zhuang, Jindong Wang +5
For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. Previous deep domain adaptation methods mainly learn a…
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Yongchun Zhu, Yudan Liu, Ruobing Xie +6
In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display,…
Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
Dongbo Xi, Zhen Chen, Peng Yan +4
In most real-world large-scale online applications (e.g., e-commerce or finance), customer acquisition is usually a multi-step conversion process of audiences. For example, an impr…
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang +5
Recently, embedding techniques have achieved impressive success in recommender systems. However, the embedding techniques are data demanding and suffer from the cold-start problem.…