王翔
Email: xiangwang@ustc.tsg211.com
个人主页: http://staff.ustc.tsg211.com/~xiangwang/
地址: 中科大高新校区，信智大楼B404
主要研究兴趣：
My research interests include information retrieval, data mining, and trustworthy and explainable AI, particularly in recommender systems, graph learning, and social media analysis. I have over 40 publications appeared in several top conferences (e.g., NeurIPS, ICLR, SIGIR, WWW, KDD, CVPR, IEEE S&P) and journals (e.g., TKDE, TOIS). Moreover, I have served as the PC member for top-tier conferences including NeurIPS, ICLR, SIGIR and KDD, and the invited reviewer for prestigious journals including JMLR, TKDE, TOIS, TKDD, and TIST.
招生信息：
1. Hiring tenure-track faculties and postdocs in NLP/IR/DM. Requirements:
- With PhD degree (or graduate soon)
- At least three first-author papers on tier-1 conferences
2. Hiring PhD students from USTC and masters. Requirements:
- Strong code ability (C/C++ or Python)
- English (CET-6 score 500+, or equal levels)
- Determination to do high-quality research.
3. Hiring master students and undergraduate interns. Requirements:
- Strong code ability (C/C++ or Python)
- Determination to do high-quality research.
- Experience in high-level competitions (e.g., ACM-ICPC and KDD-Cup) will be considered.
教育经历：
Sep 2010 - June 2014, Bachelor in Computer Science and Technology, Beihang University (BUAA), China
July 2014 - Feb 2019, Ph.D. in Computer Science, National University of Singapore (NUS), Singapore
研究经历：
Feb 2022 - Present, Professor, University of Science and Technology of China
July 2021 - Feb 2022, Senior Research Fellow, National University of Singapore
Feb 2019 - July 2021, Research Fellow, National University of Singapore
主要论著：
[01] Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua. Neural Graph Collaborative Filtering. In SIGIR 2019.
[02] Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD 2019.
[03] Xiang Wang, Xiangnan He, Liqiang Nie, Tat-Seng Chua. Item Silk Road: Recommending Items from Information Domains to Social Users. In SIGIR 2017.
[04] Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, TatSeng Chua. ExplainableReasoning over Knowledge Graphs for Recommendation. In AAAI 2019.
[05] Xiang Wang, Xiangnan He, Fuli Feng, Liqiang Nie, Tat-Seng Chua. TEM: Tree-enhanced Embedding Model for Explainable Recommendation. In WWW 2018.
[06] Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He* & Tat-Seng Chua. Towards Multi-Grained Explainability for Graph Neural Networks. In NeurIPS 2021.
[07] Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He & Tat-Seng Chua. Discovering Invariant Rationales for Graph Neural Networks. In ICLR 2022.
[08] Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie. Self-supervised Graph Learning for Recommendation. In SIGIR 2021.
[09] Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, Tat-Seng Chua. Learning Intents behind Interactions with Knowledge Graph for Recommendation. In WWW 2021.
[10] Xiaoyu Du, Xiang Wang, Xiangnan He, Zechao Li, Jinhui Tang, Tat-Seng Chua. How to Learn Item Representation for Cold-Start Multimedia Recommendation? In ACM MM 2020.
[11] Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Tat-Seng Chua. Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback. In ACM MM 2020.
[12] Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen, Tat-Seng Chua. Interactive Path Reasoning on Graph for Conversational Recommendation. In KDD 2020.
[13] Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-Seng Chua. Disentangled Graph Collaborative Filtering. In SIGIR 2020.
[14] Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, Meng Wang. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR 2020.
[15] Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua. Hierarchical Fashion Graph Network for Personalized Outfit Recommendation. In SIGIR 2020.
[16] Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua. Reinforced Negative Sampling over Knowledge Graph for Recommendation. In WWW 2020.
[17] Jingjing Chen, Liangming Pan, Zhipeng Wei, Xiang Wang, Chong-Wah Ngo, Tat-Seng Chua. Zero-shot Ingredient Recognition by Multi-Relational Graph Convolutional Network. In AAAI 2020.
[18] Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, Tat-Seng Chu. MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video. In ACM MM 2019.
[19] Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang, Tat-Seng Chua. Interpretable Fashion Matching with Rich Attributes. In SIGIR 2019.
[20] Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, Tat-Seng Chua. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences. In WWW 2019.
[21] Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua. Outer Product-based Neural Collaborative Filtering. In IJCAI 2018.
[22] Fuli Feng, Liqiang Nie, Xiang Wang, Richang Hong, Tat Seng Chua. Computational Social Indicators: A Case Study of Chinese University Ranking. In SIGIR 2017.
[23] Yinwei Wei, Xiang Wang, Weili Guan, Liqiang Nie, Zhouchen Lin, Baoquan Chen. Neural Multimodal Cooperative Learning Toward Micro-Video Understanding. In TIP 2019.
[24] Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong. Deep Item-based Collaborative Filtering for Top-N Recommendation. In TOIS 2019.
[25] Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat Seng Chua. Temporal Relational Ranking for Stock Prediction. In TOIS 2019.
[26] Meng Liu, Liqiang Nie, Xiang Wang, Qi Tian, Baoquan Chen. Online Data Organizer: Micro-Video Categorization by Structure-Guided Multimodal Dictionary Learning. In TIP 2018.
[27] Xiang Wang, Liqiang Nie, Xuemeng Song, Dongxiang Zhang, Tat-Seng Chua. Unifying Virtual and Physical Worlds: Learning Toward Local and Global Consistency. In TOIS 2017.
[28] Qi Wan, Xiangnan He, Xiang Wang, Jiancan Wu, Wei Guo, Ruiming Tang. Cross Pairwise Ranking for Unbiased Item Recommendation. In WWW 2022.