News
October 2023
One paper is accepted by WSDM'24 about Label Learning for Micro-video Recommendation.
October 2023
Four papers are accepted by EMNLP'23 about Prompt Tuning, Robust Prompt Optimization, Adversarial Training, and Safety of Large Language Models (LLMs).
August 2023
Two papers are accepted by NeurIPS'23 about Recommendation Fairness and Causal Recommendation.
August 2023
One paper is accepted by CIKM'23 about Causal Recommendation.
June 2023
Four papers are accepted by RecSys'23 about LLM for Recommendation and Drug Reaction Prediction.
June 2023
Four papers are accepted by RecSys'23 about LLM for Recommendation and Drug Reaction Prediction.
June 2023
One paper is accepted by TKDE about Causal Distillation for Recommendation Debias.
May 2023
One paper is accepted by SIGKDD'23 about O.O.D generalization on graphs.
May 2023
Four papers are accepted by ACL'23 about O.O.D generalization, counterfactual thinking, and prompt tuning.
April 2023
Three papers are accepted by SIGIR'23 about recommendation with invariant learning, diffusion model, and retrospective thinking.
April 2023
One paper is accepted by TOIS about causal recommendation and O.O.D. generalization.
April 2023
One paper is accepted by IJCAI'23 about recommendation debias with domain adaptation.
February 2023
One paper is accepted by TPAMI about causal recommendation with causal structure learning.
January 2023
Three papers are accepted by WWW'23 about negative sampling and shilling attack in recommendation, and data poisoning attack in knowledge graph embedding.
October 2022
Two full papers are accepted by WSDM'23 about causal recommendation with debiased model distillation and causal effect estimation.
August 2022
One paper is accepted by TOIS about causal recommendation and recommendation debias.
July 2022
Two full papers are accepted by ACMMM'22 about OOD recommendation with invariant representation learning and descrite reasoning over complex document.
May 2022
Two full papers are accepted by SIGKDD'22 about causal recommendation and GCN debias.
April 2022
Three full papers are accepted by SIGIR'22 about filter bubble and bias issues in recommendation and conversational search.
March 2022
One paper is accepted by TNNLS about GCN retraining and causal recommendation.
February 2022
One full paper is accepted by ACL'22 main conference about causal reasoning.
January 2022
Four full papers are accepted by WWW'22 about causal representation learning, OOD recommendation, recommendation denoise, multi-interest modeling and Web-page language model.
December 2021
One paper is accepted by TKDE about GCN for categorical node features.
October 2021
Onw full paper is accepted by ICDE'22 about financial text understanding.
July 2021
Two full papers are accepted by ICAIF'21 (ACM International Conference on AI in Finance) about financial text understanding. One full paper is accepted by CIKM'21 about time-series prediction.
May 2021
Three full papers are accepted by ACL'21 about causal reasoning and discrete reasoning. Two papers are accepted by SIGKDD'21 about causal reasoning and recommendation debias.
April 2021
One paper is accepted by TKDE about graph neural network. Six full papers are accepted by SIGIR'21 about causal reasoning, self-supervised learning, and financial event ranking.
March 2021
We release a large-scale dataset for few-shot graph classification.
January 2021
One full paper is accepted by WWW'21 about graph neural network.
December 2020
One full paper is accepted by AAAI'21 about text representation learning. One full paper is accepted by WSDM 2021 about denoising implicit feedback. One full paper is accepted by ICDM 2020 about cold-start recommendation.
November 2020
I am invited to be a senior program committee member in The International Joint Conference on Artificial Intelligence (IJCAI) 2021, and a program committee member in International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) 2021 .
June 2020
I am invited to be a program committee member in The Conference on Neural Information Processing Systems (NeurIPS) 2020, The 14th ACM International Web Search and Data Mining Conference (WSDM) 2021 , The Conference on Empirical Methods in Natural Language Processing (EMNLP) 2020.
April 2020
Two full papers are accepted by SIGIR'20, about progressive recommender training and low-resource text classification. One full papre is accepted by IJCAI'20, about graph neural network.
December 2019
I am invited to be a program committee member in Annual Conference of the Association for Computational Linguistics (ACL) 2020 and International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) 2020. One full paper is accepted by AAAI'20 (oral), about sequential text classification.
November 2019
One full paper is accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE), about adversarial training of graph neural networks. One full paper is accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE), about multi-behaviour recommendation.
October 2019
I am invited to be a program committee member in The Web Conference (WWW) 2020.
September 2019
One full paper is accepted by IEEE Transactions on Multimedia (TMM), about food recommendation.
August 2019
I am invited to be a program committee member in AAAI 2020.
August 2019
I have successfully defended my thesis and got the PhD degree! My thesis title is "Learning on Graphs".
July 2019
One full paper is accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE), about negative sampling for recommendation.
May 2019
I am invited to be a program committee member in CIKM 2019.
April 2019
One full paper is accepted by IJCAI'19, about adversarial training for stock prediction. Three full papers are accepted by SIGIR'19, about graph neural network for recommendation, interpretable fashion matching, and hierarchical hashing.
January 2019
One full paper is accepted by ACM Transactions on Information Systems (TOIS), about graph neural network for stock prediction.
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Fuli Feng
Professor (博导)
School of Data Science
100, Fuxing Road, Hefei, China
fulifeng93 AT gmail.com, fengfl AT ustc.edu.cn
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I am a Professor (博导) in University of Science and Technology of China. I received B.Eng. from Beihang University in July 2015 and PhD from National University of Singapore in August 2019. I have about
100
publications appeared in several top conferences such as SIGIR, SIGKDD, WWW, and ACL, and journals including TKDE, TOIS, and TPAMI. I received the Best Paper Honourable Mention in SIGIR 2021 and Best Poster Award in WWW 2018. My research interests include information retrieval, data mining, and multi-media analytics, particularly in machine learning techniques and applications such as causal inference, graph neural networks, adversarial learning, multi-source learning, recommender systems, FinTech, and text mining. Moreover, I have served as the AC or SPC/PC-member for top-tier conferences including SIGIR, WWW, SIGKDD, NeurIPS, ICLR, ICML, ACL, and the invited reviewer for prestigious journals such as TOIS, TKDE, TPAMI, TNNLS, JASA, and Nature Sustainability.
Advertisements:
0. The 1ST Workshop on Recommendation with Generative Models:
- CIKM'23, Birmingham UK
- Website
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
We provide competitive salary, sufficient funding and student supports, and good career opportunities.
2. Hiring PhD students from USTC and masters. Requirements:
- Strong code ability (C/C++ or Python)
- English (CET-6 score 500+ or equal levels or proficient in using ChatGPT or similar tools)
- Determination to do high-quality research.
3. Hiring undergraduate interns. Requirements:
- Strong code ability (C/C++ or Python)
- Determination to do high-quality research or interested in practical applications
- Experience in high-level competitions (e.g., ACM-ICPC and KDD-Cup) will be considered.
I hope every student can receive the core ability of systematically analyzing and solving research/technical problems, meanwhile, have a colorful and healthy life during the study in my group. I encourage students to be communicative and active in the group. I support various group activities, especially sports like badminton, jogging, and basketball. Meanwhile, I will try my best to create internship or visiting opportunities for students seeking such experience.
Advertisements:
0. The 1ST Workshop on Recommendation with Generative Models:
- CIKM'23, Birmingham UK
- Website
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
We provide competitive salary, sufficient funding and student supports, and good career opportunities.
2. Hiring PhD students from USTC and masters. Requirements:
- Strong code ability (C/C++ or Python)
- English (CET-6 score 500+ or equal levels or proficient in using ChatGPT or similar tools)
- Determination to do high-quality research.
3. Hiring undergraduate interns. Requirements:
- Strong code ability (C/C++ or Python)
- Determination to do high-quality research or interested in practical applications
- Experience in high-level competitions (e.g., ACM-ICPC and KDD-Cup) will be considered.
I hope every student can receive the core ability of systematically analyzing and solving research/technical problems, meanwhile, have a colorful and healthy life during the study in my group. I encourage students to be communicative and active in the group. I support various group activities, especially sports like badminton, jogging, and basketball. Meanwhile, I will try my best to create internship or visiting opportunities for students seeking such experience.
Selected Publications
I usually forgot to update my profile timely, please find my latest work at Google ScholarIn the Year of 2023:
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LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting
Yimeng Bai, Yang Zhang, Jing Lu, Jianxin Chang, Xiaoxue Zang, Yanan Niu, Yang Song and Fuli Feng WSDM (Poster) |
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Robust Prompt Optimization for Large Language Models Against Distribution Shifts
Moxin Li, Wenjie Wang, Fuli Feng, Yixin Cao, Jizhi Zhang and Tat-Seng Chua EMNLP (Main) |
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Robust Prompt Optimization for Large Language Models Against Distribution Shifts
Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang and Xiangnan He EMNLP (Finding) |
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Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach
Jinqiu Jin, Haoxuan Li, Fuli Feng, Sihao Ding, Peng Wu and Xiangnan He NeurIPS (Poster) |
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Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach
Haoxuan Li, Kunhan Wu, Chunyuan Zheng, Yanghao Xiao, Hao Wang, Fuli Feng, Xiangnan He, Zhi Geng and Peng Wu NeurIPS (Poster) |
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Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework
Yang Zhang, Yimeng Bai, Jianxin Chang, Xiaoxue Zang, Song Lu, Jing Lu, Fuli Feng, Yanan Niu and Yang Song CIKM 2023 (Industry) |
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Can ChatGPT Make Fair Recommendation? A Fairness Evaluation Benchmark for Recommendation with Large Language Model
Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng and Xiangnan He RecSys 2023 (Short) |
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LLM4Rec: Large Language Models for Recommendation via A Lightweight Tuning Framework
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng and Xiangnan He RecSys 2023 (Short) |
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RecAD : Towards A Unified Library for Recommender Attack and Defense
Changsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng and Xiangnan He RecSys 2023 (Reproducibility) |
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Counterfactual Active Learning for Out-of-Distribution Generalization
Xun Deng, Wenjie Wang, Fuli Feng, Hanwang Zhang, Xiangnan He and Yong Liao ACL 2023 |
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Hypothetical Training for Robust Machine Reading Comprehension of Tabular Context
Moxin Li, Wenjie Wang, Fuli Feng, Hanwang Zhang, Qifan Wang and Tat-Seng Chua ACL 2023 (Finding) |
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MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction
Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu and Dongfang Liu ACL 2023 |
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Diffusion Recommender Model
Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, Tat-Seng Chua SIGIR 2023 (Full) |
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Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
Yang Zhang, Tianhao Shi, Fuli Feng, Wenjie Wang, Dingxian Wang, Xiangnan He, Yongdong Zhang SIGIR 2023 (Full) |
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Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation
Yulong Huang, Yang Zhang, Qifan Wang, Chenxu Wang, Fuli Feng SIGIR 2023 (Short) |
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FLOOD: A Flexible Invariant Learning Framework for Out-of-Distribution Generalization on Graphs
Yang Liu, Xiang Ao, Fuli Feng, Yunshan Ma, Kuan Li, Tat-Seng Chua, Qing He SIGKDD 2023 (Full) |
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On the Theories Behind Hard Negative Sampling for Recommendation
Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao and Xiangnan He WWW 2023 (Full) |
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Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model
Xiaoyu You, Chi Li, Daizong Ding, Mi Zhang, Fuli Feng, Xudong Pan, Min Yang WWW 2023 (Full) |
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MaSS: Model-agnostic, Semantic and Stealthy Data Poisoning Attack on Knowledge Graph Embedding
Xiaoyu You, Beina Sheng, Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Fuli Feng WWW 2023 (Full) |
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A Causal View for Item-level Effect of Recommendation on User Preference
Wei Cai, Fuli Feng, Qifan Wang, Tian Yang, Zhenguang Liu and Congfu Xu WSDM 2023 (Full) |
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Unbiased Knowledge Distillation for Recommendation
Gang Chen, Jiawei Chen, Fuli Feng, Sheng Zhou and Xiangnan He WSDM 2023 (Full) |
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Personalized Latent Structure Learning for Recommendation
Shengyu Zhang, Fuli Feng, Kun Kuang, Wenqiao Zhang, Zhou Zhao, Hongxia Yang, Tat-Seng Chua and Fei Wu IEEE TPAMI |
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Causal Disentangled Recommendation Against User Preference Shifts
Wenjie Wang, Xinyu Lin, Liuhui Wang, Fuli Feng, Yunshan Ma, Tat-Seng Chua ACM TOIS |
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Mitigating Spurious Correlations for Self-supervised Recommendation
Xinyu Lin, Yiyan Xu, Wenjie Wang, Yang Zhang, Fuli Feng Machine Intelligence Research |
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Addressing Confounding Feature Issue for Causal Recommendation
Xiangnan He, Yang Zhang, Fuli Feng, Chonggang Song, Lingling Yi, Guohui Ling and Yongdong Zhang ACM TOIS 2022 |
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Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis
Sihao Ding, Peng Wu, Fuli Feng, Yitong Wang, Xiangnan He, Yong Liao and Yongdong Zhang SIGKDD 2022 (Full) |
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UD-GNN: Uncertainty-aware Debiased Training on Semi-Homophilous Graphs
Yang Liu, Xiang Ao, Fuli Feng, Qing He SIGKDD 2022 (Full) |
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Interpolative Distillation for Unifying Biased and Debiased Recommendation
Sihao Ding, Fuli Feng, Xiangnan He, Jinqiu Jin, Wenjie Wang, Yong Liao and Yongdong Zhang SIGIR 2022 (Full) |
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User-controllable Recommendation Against Filter Bubbles
Wenjie Wang, Fuli Feng, Liqiang Nie and Tat-Seng Chua SIGIR 2022 (Full) |
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Structured and Natural Responses Co-generation for Conversational Search
Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji and Tat-Seng Chua SIGIR 2022 (Full) |
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Invariant Representation Learning for Multimedia Recommendation
Xiaoyu Du, Zike Wu, Fuli Feng, Xiangnan He, and Jinhui Tang ACMMM 2022 (Full) |
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Towards Complex Document Understanding by Discrete Reasoning
Fengbin Zhu, Wenqiang Lei, Fuli Feng, Chao Wang, Haozhou Zhang and Tat-Seng Chua ACMMM 2022 (Full) |
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Learning to Double-check Model Prediction from a Causal Perspective
Xun Deng, Fuli Feng, Xiang Wang, Xiangnan He, Hanwang Zhang and Tat-Seng Chua IEEE TNNLS 2022 (2nd Round Review) |
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Causal Incremental Graph Convolution for Recommender System Retraining
Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi and Yongdong Zhang IEEE TNNLS 2022 |
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Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning
Moxin Li, Fuli Feng, Hanwang Zhang, Xiangnan He, Fengbin Zhu and Tat-Seng Chua ACL 2022 (Full, main conference) |
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Causal Representation Learning for Out-of-Distribution Recommendation
Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin and Tat-Seng Chua WWW 2022 (Full) |
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Learning Robust Recommenders through Cross-Model
Yu Wang, Xin Xin, Zaiqiao Meng, Joemon Jose, Fuli Feng and Xiangnan He WWW 2022 (Full) |
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WebFormer: The Web-page Transformer for Structure Information Extraction
Qifan Wang, Yi Fang, Anirudh Ravula, Fuli Feng, Xiaojun Quan and Dongfang Liu WWW 2022 (Full) |
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Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation
Shengyu Zhang, Lingxiao Yang, Dong Yao, Yujie Lu, Fuli Feng, Zhou Zhao, Tat-Seng Chua and Fei Wu WWW 2022 (Full) |
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Towards Backdoor Attack on Deep Learning based Time Series Classification
Daizong Ding, Mi Zhang, Yuanmin Huang, Xudong Pan, Fuli Feng, Erling Jiang and Min Yang ICDE 2022 (Full) |
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Dynamic Hypergraph Convolutional Network
Nan Yin, Fuli Feng, Zhigang Luo, Xiang Zhang, Wenjie Wang, Xiao Luo, Chong Chen and Xian-Sheng Hua ICDE 2022 (Full) |
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Response Generation by Jointly Modeling Personalized Linguistic Styles and Emotions
Teng Sun, Chun Wang, Xuemeng Song, Fuli Feng and Liqiang Nie ACM TOMM 2022 |
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Deconfounded recommendation for alleviating bias amplification
Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang and Tat-Seng Chua SIGKDD 2021 (Full) |
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Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system
Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi and Xiangnan He SIGKDD 2021 (Full) |
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Causal intervention for leveraging popularity bias in recommendation
Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling and Yongdong Zhang SIGIR 2021 (Full) |
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Clicks Can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang and Tat-Seng Chua SIGIR 2021 (Full) |
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Deconfounded video moment retrieval with causal intervention
Xun Yang, Fuli Feng, Wei Ji, Meng Wang and Tat-Seng Chua SIGIR 2021 (Full) |
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Should graph convolution trust neighbors? a simple causal inference method
Fuli Feng, Weiran Huang, Xiangnan He, Xin Xin, Qifan Wang and Tat-Seng Chua SIGIR 2021 (Full) |
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Self-supervised Graph Learning for Recommendation
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian and Xing Xie SIGIR 2021 (Full) |
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Hybrid learning to rank for financial event ranking
Fuli Feng, Moxin Li, Cheng Luo, Ritchie Ng, Tat-Seng Chua SIGIR 2021 (Full) |
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Empowering Language Understanding with Counterfactual Reasoning
Fuli Feng, Jizhi Zhang, Xiangnan He, Hanwang Zhang and Tat-Seng Chua ACL 2021 (Findings) Codes |
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TAT-QA: A question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng and Tat-Seng Chua ACL 2021 (Full) Codes and Data |
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Counterfactual Inference for Text Classification Debiasing
Chen Qian, Fuli Feng, Lijie Wen, Chunping Ma and Pengjun Xie ACL 2021 (Full) |
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On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
Hande Dong, Jiawei Chen, Fuli Feng, Xiangnan He, Shuxian Bi, Zhaolin Ding and Peng Cui WWW 2021 (Full) Codes |
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Conceptualized and Contextualized Gaussian Embedding
Chen Qian, Fuli Feng, Lijie Wen and Tat-Seng Chua AAAI 2021 (Full) Codes |
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Denoising Implicit Feedback for Recommendation
Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie and Tat-Seng Chua WSDM 2021 (Full) Codes |
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Learning to Learn the Future: Modeling Concept Drifts in Time Series Prediction
Xiaoyu You, Mi Zhang, Daizong Ding, Fuli Feng and Yuanmin Huang CIKM 2021 (Full) |
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Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions
Fuli Feng, Xiangnan He, Hanwang Zhang and Tat-Seng Chua TKDE 2021 |
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CatGCN: Graph Convolutional Networks with Categorical Node Features
Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling and Yongdong Zhang TKDE 2021 |
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Cross-domain Recommendation with Bridge-Item Embeddings
Chen Gao, Yong Li, Fuli Feng, Xiangning Chen, Kai Zhao, Xiangnan He and Depeng Jin TKDD 2021 |
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Attribute-wise Explainable Fashion Compatibility Modeling
Xin Yang, Xuemeng Song, Fuli Feng, Haokun Wen, Ling-Yu Duan and Liqiang Nie ACM TOMM 2021 |
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Urban Perception: Sensing Cities via a Deep Interactive Multi-task Learning Framework
Weili Guan, Zhaozheng Chen, Fuli Feng, Weifeng Liu and Liqiang Nie ACM TOMM 2021 |
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Structure-enhanced Meta-learning for Few-shot Graph Classification
Shunyu Jiang, Fuli Feng, Weijian Chen, Xiang Li and Xiangnan He AI Open 2021 (Full) Code and Data |
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Heterogeneous Fusion of Semantic and Collaborative Information for Visually-Aware Food Recommendation
Lei Meng, Fuli Feng, Xiangnan He, Xiaoyan Gao and Tat-Seng Chua ACMMM 2020 (Full) |
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How to Retrain Recommender System? A Sequential Meta-Learning Approach
Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li and Yongdong Zhang SIGIR 2020 (Full) Codes |
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Enhancing Text Classification via Discovering Additional Semantic Clues from Logograms
Chen Qian, Fuli Feng, Lijie Wen, Li Lin, & Tat-Seng Chua SIGIR 2020 (Full, Oral) Codes |
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Bilinear Graph Neural Network with Neighbor Interactions
Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng and Yongdong Zhang IJCAI 2020 (Full) Codes |
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Solving Sequential Text Classification as Board-Game Playing
Chen Qian, Fuli Feng, Lijie Wen, Zhenpeng Chen, Li Lin, Yanan Zheng and Tat-Seng Chua AAAI 2020 (Full, Oral) Codes |
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Large-Scale Question Tagging via Joint Question-Topic Embedding Learning
Liqiang Nie, Yongqi Li, Fuli Feng, Xuemeng Song, Meng Wang and Tat-Seng Chua ACM Transactions on Information Systems (TOIS, 2020) Codes |
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Fast Adaptation for Cold-Start Collaborative Filtering with Meta-Learning
Tianxin Wei, Ziwei Wu, Ruirui Li, Ziniu Hu, Fuli Feng, Xiangnan He; Yizhou Sun and Wei Wang ICDM 2020 (Full) |
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Learning to Recommend with Multiple Cascading Behaviors
Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua and Depeng Jin IEEE Transactions on Knowledge and Data Engineering (TKDE 2019) |
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Hierarchical Attention Network for Visually-aware Food Recommendation
Xiaoyan Gao, Fuli Feng, Xiangnan He, Heyan Huang, Xinyu Guan, Chong Feng Zhaoyan Ming and Tat-Seng Chua IEEE Transactions on Multimedia (TMM 2019) Data |
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Enhancing Stock Movement Prediction with Adversarial Training
Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun and Tat-Seng Chua IJCAI 2019 (Full, Accept rate: 17.9%) Codes |
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Interpretable Fashion Matching with Rich Attributes
Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang and Tat-Seng Chua SIGIR 2019 (Full, Accept rate: 20%) Slides |
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Neural Graph Collaborative Filtering
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng and Tat-Seng Chua SIGIR 2019 (Full, Accept rate: 20%) • arXiv • Codes |
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Supervised Hierarchical Cross-Modal Hashing
Changchang Sun, Xuemeng Song, Fuli Feng, Wayne Xin Zhao, Hao Zhang and Liqiang Nie SIGIR 2019 (Full, Accept rate: 20%) |
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Cross-domain Recommendation Without Sharing User-relevant Data
Chen Gao, Xiangning Chen, Fuli Feng, Kai Zhao, Xiangnan He, Yong Li and Depeng Jin WWW 2019 (Full, Accept rate: 18%) |
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Explicit Interaction Model towards Text Classification
Cunxiao Du, Zhaozheng Chin, Fuli Feng, Lei Zhu, Tian Gan and Liqiang Nie AAAI 2019 (Full, Accept rate: 16.2%) |
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Neural Multi-Task Recommendation from Multi-Behavior Data
Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua and Depeng Jin ICDE 2019 (Short) |
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Temporal Relational Ranking for Stock Prediction
Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu and Tat-Seng Chua ACM Transactions on Information Systems (TOIS 2019) Codes |
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Sampler Design for Bayesian Personalized Ranking by Leveraging View Data
Jingtao Ding, Guanghui Yu, Xiangnan He, Fuli Feng, Yong Li and Depeng Jin IEEE Transactions on Knowledge and Data Engineering (TKDE 2019) |
Invited Talks
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Temporal Relational Ranking for Stock Prediction
- MegaAI Company, Beijing, 13 February, 2019 (invited by Dr. Cheng Luo) - Shandong University, 11 Augest, 2018 (invited by Prof. Liqiang Nie) |
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Learning on Partial-Order Hypergraphs
- WWW 2018, Lyon, France, 26 April, 2018 |
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Computational Social Indicators: A Case Study of Chinese University Ranking
- SIGIR 2017, Tokyo, Japan, 5 August, 2017 |
Professional Services
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Program Committee Member of AAAI (2020) Program Committee Member of CIKM (2019) Program Committee Member of SIGIR (2019) Program Committee Member of ACMMM (2019) Program Committee Member of SIGIR (2018) Invited Reviewer for IEEE Transactions on Neural Networks and Learning Systems (TNNLS) Invited Reviewer for IEEE Transactions on Knowledge and Data Engineering (TKDE) Invited Reviewer for Information Sciences Invited Reviewer for Multimedia Tools and Applications Invited Reviewer for World Wide Web Journal (WWWJ) Invited Reviewer for Multimedia Systems Journal (MMSJ) External Reviewer of WWW and WSDM in 2019. External Reviewer of AIRS, EMNLP, CIKM, ACMMM, ACL, IJCAI, KDD, AAAI, and WSDM in 2018. External Reviewer of AIRS, EMNLP, CIKM, ACMMM, ACL, IJCAI, KDD, AAAI, and WSDM in 2017. |
Education
| National University of Singapore (NUS) Ph.D. in Computer Science August 2015 - August 2019, Singapore Advisor: Prof. Tat-Seng Chua Mentors: Prof. Xiangnan He and Prof. Liqiang Nie |
| Beihang University (BUAA) Bachelor in Computer Science and Engineering September 2011 - June 2015, Beijing Advisor: Prof. Zhoujun Li |
Experiences
| Professor, University of Science and Technology of China, February 2022 - Present |
| Postdoc Research Fellow, National University of Singapore, June 2019 - January 2022 Advisior: Prof. Tat-Seng Chua (NExT++: NUS-Tsinghua-Southampton Extreme Search Center) |
Selected Awards
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Dean's Graduate Research Excellence Award, 2018
- School of Computing, National University of Singapore |
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Best Poster Award, 2018
- The Web Conference (WWW) |
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Research Achievement Award, 2017
- School of Computing, National University of Singapore |
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Outstanding Undergraduate Award, 2014
- China Computer Federation (CCF) |
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1st Prize Award, 2014
- ASC World Student Supercomputer Challenge |
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Application Innovation Award, 2014
- ASC World Student Supercomputer Challenge |
Useful Links
| NUS CS Conference Rankings |
| NUS CS Journal Ranking |
| NUS CS Courses |
| Machine Learning Reading List |
| Deep Learning Reading List |
Last update: 25 August, 2019. Webpage template borrows from Prof. Xiangnan He.