38 citations · 42 across the 7 of their papers we have counts for
7 papers
DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
The graph-based recommendation has achieved great success in recent years. However, most existing graph-based recommendations focus on capturing user preference based on positive e…
ID-centric Pre-training for Recommendation
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
Classical sequential recommendation models generally adopt ID embeddings to store knowledge learned from user historical behaviors and represent items. However, these unique IDs ar…
Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation
Meng Yuan, Fuzhen Zhuang, Zhao Zhang +2
Since Knowledge Graphs (KGs) contain rich semantic information, recently there has been an influx of KG-enhanced recommendation methods. Most of existing methods are entirely desig…
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…
A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis
Wei Chen, Jinglong Du, Zhao Zhang +2
Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect bo…
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…
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…