News
26 Sep 2024
Two papers are accepted by NeurIPS on DPO for LLM and LLM for Recsys.
26 March 2024
Five full papers are accepted by SIGIR on recommendation topics of LLM, medication, and fairness.
2 Feb 2024
Three papers are accepted by WWW on LLM-based recommendation and graph condensation, and one short paper about proactive recommendation.
16 Jan 2024
Two papers are accepted by ICLR on LLM for molecule and recommendation debiasing.
9 Dec 2023
Two papers are accepted by AAAI on few-shot learning and text-to-image generation.
20 Nov 2023
I am invited to serve as the associate editor for IEEE Transactions on Knowledge and Data Engineering (TKDE).
22 Sep 2023
Six papers are accepted by NeurIPS on generative recsys, fairness, causal rec, ood generalization, gnn explanation, etc.
27 July 2023
One paper is accepted by IEEE Transactions on Big Data (TBD) on uncertainty modeling for recommendation.
26 July 2023
Our SIGIR'23 paper "Alleviating Matthew Effect of Offline Reinforcement Learning in Recommendation" receives Best Paper Honorable Mention.
26 July 2023
Two papers are accepted by ACM MM on few shot learning and CLIP-based image captioning.
July 2023
Four papers are accepted by Recsys on Large Language Models (LLMs) and Drug Reaction Prediction.
May 2023
One paper is accepted by ACL on counterfactual learning for OOD.
20 April 2023
One paper is accepted by IJCAI on invariant learning for unbiased recsys.
9 April 2023
One paper is accepted by ACM TOIS on filter bubble issue in interactive recsys.
6 April 2023
Four papers are accepted by SIGIR 2023 on various topics of recsys, e.g., offline RL, OOD robustness, diffusion models, etc.
31 March 2023
One paper is accepted by IEEE TNNLS on counterfactual prediction.
28 March 2023
One paper is accepted by CVPR on OOD for zero-shot learning.
26 Jan 2023
Four papers are accepted by WWW on graph anomaly detection, graph unlearning, and recsys.
22 Nov 2022
One paper is accepted by AAAI on group theory for knowledge graph reasoning.
18 Oct 2022
Three papers are accepted by WSDM on unbiased recsys distillation, gnn explanation, and graph anomaly detection.
23 Aug 2022
One paper on causal recommendation is accepted by ACM TOIS.
3 Aug 2022
Two dataset papers on recsys are accepted by CIKM.
3 July 2022
Five papers are accepted by ACM Multimedia.
10 June 2022
I serve as the associate editor for ACM Transactions on Recommender Systems (TORS).
19 May 2022
Two papers are accepted by KDD on causal learning for recsys and graph neural net.
16 May 2022
One paper is accepted by ICML on invariant graph learning.
20 April 2022
One paper is accepted by TPAMI on GNN Explanation.
31 March 2022
One paper is accepted by SIGIR on debiased recommendation.
3 March 2022
One paper is accepted by CVPR on video recognition.
24 Feb 2022
One paper is accepted by ACL on causal reasoning.
25 Jan 2022
I serve as the associate editor for IEEE Transactions on Big Data (TBD).
21 Jan 2022
One paper is accepted by ICLR on GNN explanation.
15 Jan 2022
Four papers are accepted by WWW on robustness and fairness of recsys and personalized search.
1 Dec 2021
One paper is accepted by TKDE on GCN for categorical node features.
1 Dec 2021
One paper is accepted by TKDE on GCN for categorical node features.
29 Sep 2021
One paper is accepted by NeurIPS 2021 on graph neural network explanations.
8 Aug 2021
Two papers are accepted by CIKM 2021 on disentangled graph learning and hierarchical community detection.
14 July 2021
Our SIGIR'21 paper "Causal Intervention for Leveraging Popularity Bias in Recommendation" receives Best Paper Honorable Mention.
5 July 2021
One paper is accepted by ACM Multimedia 2021 on classifying actions in videos.
6 June 2021
I serve as the associate editor for ACM Transactions on Information Systems (TOIS).
16 May 2021
Two papers are accepted by KDD, on causal inference for recsys.
15 April 2021
Five papers are accepted by SIGIR, on self-supervised, causal inference and debias for recsys and graph learning.
16 Jan 2021
Three papers are accepted by WWW, on gcn vs. label propagation, knowledge graph for recsys and causal embeddings for recsys.
1 Jan 2021
We will give a tutorial Bias Issues and Solutions in Recsys on WWW 2021.
30 Dec 2020
One paper is accepted by TOIS, on conversational recsys for cold users with EE tradeoff.
16 Nov 2020
One full paper is accepted by WSDM, on denoising implicit data for recsys.
16 May 2020
Two full papers are accepted by KDD, on conversational recsys and enterprise competition analysis.
24 April 2020
One tutorial about conversational recsys is accepted by SIGIR!.
23 April 2020
Two full papers from my USTC group are accepted by SIGIR, on GCN and meta-learning for recsys. Congrats to all!
20 April 2020
One paper is accepted by IJCAI about enhancing GCN with neighbor interactions.
9 April 2020
I serve as Editorial Board member of the AI Open journal. Welcome to submit!
11 Jan 2020
Two papers are accepted by WWW about knowledge graph-enhanced negative sampling and sequential recommendation.
28 Nov 2019
Two papers are accepted by TKDE about adversarial training on graph and multi-behavior recommendation.
11 Nov 2019
Two papers are accepted by AAAI 2020 about robust network embedding and unfollow prediction in social network.
11 Oct 2019
One paper is accepted by WSDM 2020 about conversational recommender system.
2 Oct 2019
Two papers are accepted by ICDE 2020 about price-aware recommendation recommendation, and neural relation extraction.
28 July 2019
Two papers are accepted by TKDE about group recommendation, and negative sampler with view data.
26 July 2019
One paper is accepted by TMM about multi-modal social event analysis, and one paper is accepted by ICCV 2019 (oral) about scene graph generation.
26 July 2019
One paper is accepted by TOIS on extending convolutional neural collaborative filtering.
11 May 2019
Three full papers are accepted by ACM Multimedia 2019, on micro-video recommendation, mix-dish recognition, and image subjective attributes.
30 April 2019
Four papers are accepted by KDD 2019 research track, about knowledge graph for recommendation, learning to regularize, sequential set prediction, and time series prediction, respectively.
14 April 2019
Three full papers are accepted by SIGIR 2019, about graph neural network for recommendation, knowledge-based recommendation and interpretable fashion matching, respectively.
23 Feb 2019
I am invited to be an area chair of the ACM Multimedia 2019 conference, of the multimedia search and recommendation track.
21 Jan 2019
Two papers are accepted by WWW 2019, about privacy-preserving cross-domain recommendation and knowledge graph based recommendation.
19 Jan 2019
Two papers are accepted by TOIS, about stock prediction and review-aware recommendation.
1 Jan 2019
One paper is accepted by TNNLS which explores the weights of missing data for matrix factorization.
1 Nov 2018
Two papers are accepted by AAAI 2019. One performs knowledge graph reasoning for explainable recommendation, one explores self-attention for Video QA.
22 Oct 2018
One paper about session-based recommendation is accepted by WSDM 2019.
27 Sep 2018
One paper about using multiple types of user behaviors for recommendation is accepted by ICDE 2019.
2 July 2018
Three full papers are accepted by MM 2018. One paper about multimodal dialog systems is in the Best Paper Final List.
16 May 2018
One full paper is accepted by UAI 2018.
27 April 2018
Two awards from WWW 2018! One short paper receives Best Poster Award, and one long paper receives Best Paper Award Honorable Mention.
23 April 2018
Two papers are accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE)!
22 April 2018
Call for papers: MMSJ Special Issue on Multimedia Recommendation.
21 April 2018
Two full research papers are accepted by ACL 2018!
17 April 2018
Five full research papers are accepted by IJCAI 2018!
12 April 2018
Six full research papers are accepted by SIGIR 2018!
5 March 2018
Two tutorials are accepted by SIGIR 2018!
5 March 2018
I am invited to be a program committee member in MM 2018 and CIKM 2018.
25 Feb 2018
One poster paper advised by me is accepted by WWW 2018, about negative sampler for BPR.
28 Dec 2017
I am invited to be a program committee member in KDD 2018 and SIGIR 2018.
23 Dec 2017
One full paper about entity resolution is accepted by ICDE 2018.
22 Dec 2017
Three full papers advised by me are accepted by WWW 2018! About tree+embedding for explainable recommendation, aesthetic-aware clothing recommendation, and hypergraph learning.
4 Dec 2017
Our tutorial proposal on "Deep Learning for Matching in Search and Recommendation" is accepted by WWW 2018.
3 Dec 2017
I am invited to be a program committee member in ACL 2018 and PAKDD 2018.
13 Nov 2017
I am invited to be a program committee member in IJCAI 2018.
Xiangnan HE
Professor
School of Data Science
443 Huangshan Road, Hefei, China 230027
Email: xiangnanhe at gmail.com
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I lead the USTC Lab for Data Science. My research interests span Information Retrieval, Data Mining, and Artifical General Intelligence. I have over 100 publications appeared in several top conferences such as SIGIR, WWW, and KDD, and journals including TKDE, TOIS, and TNNLS. My work has received the Best Paper Award Honourable Mention in SIGIR (2023, 2021, 2016) and WWW (2018), etc. Moreover, I have served as the Associate Editor for journals including IEEE TKDE, ACM TOIS, IEEE TBD, ACM TORS, etc., and senior PC member for conferences including SIGIR, WWW, KDD, MM, etc.
Advertisements:
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 top-tier conferences/journals
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)
- Determination to do high-quality research.
3. We recently release five surveys on different aspects (causal, bias, conversational, neural, gnn) of Recsys:
- Causal Inference in Recommender Systems: A Survey and Future Directions
- Bias and Debias in Recommender System: A Survey and Future Directions
- Advances and Challenges in Conversational Recommender Systems: A Survey
- A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation
- Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
4. We recently release an outlook paper on large language models (LLMs) for information retrieval, as an output of Chinese IR community's strategic thinking:
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Tutorials
Large Language Models for Recommendation: Progresses and Future Directions
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng & Xiangnan He SIGIR-AP 2023 WWW 2024 Slides |
Causal Recommendation: Progresses and Future Directions
Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng & Xiangnan He WWW 2022, SIGIR 2023 Slides (WWW'22) Slides (SIGIR'23) |
Graph Neural Networks for Recommender System
Chen Gao, Xiang Wang, Xiangnan He & Yong Li WSDM 2022 Slides Video |
Bias Issues and Solutions in Recommender System
Jiawei Chen, Xiang Wang, Fuli Feng & Xiangnan He WWW 2021, Recsys 2021 Slides |
Conversational Recommendation: Formulation, Methods, and Evaluation
Wenqiang Lei, Xiangnan He, Maarten de Rijke & Tat-Seng Chua SIGIR 2020, Slides |
Learning and Reasoning on Graph for Recommendation
Xiang Wang, Xiangnan He, & Tat-Seng Chua CIKM 2019, WSDM 2020 Slides |
Deep Learning for Matching in Search and Recommendation
Jun Xu, Xiangnan He, & Hang Li SIGIR 2018, WWW 2018, WSDM 2019 Slides |
Information Discovery in E-commerce
Zhaochun Ren, Xiangnan He, Dawei Yin, & Maarten de Rijke SIGIR 2018 Slides (2018/7/8 @ Ann Arbor Michigan, US) |
Recommendation Technologies for Multimedia Content
Xiangnan He, Hanwang Zhang & Tat-Seng Chua ICMR 2018 Slides (2018/6/11 @ Yokohama, Japan) |
Selected Publications
(Full list see my [Group Homepage] [Google Scholar] ) In the Year of 2024:
β-DPO: Direct Preference Optimization with Dynamic β
Junkang Wu, Yuexiang Xie, Zhengyi Yang, Jiancan Wu, Jinyang Gao, Bolin Ding, Xiang Wang& Xiangnan He* NeurIPS 2024 (Accept Rate: 25.8%) Codes *Corresponding |
Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
Xiaoyu Kong, Jiancan Wu, An Zhang, Leheng Sheng, Hui Lin, Xiang Wang & Xiangnan He* NeurIPS 2024 (Accept Rate: 25.8%) *Corresponding |
Large Language Models are Learnable Planners for Long-Term Recommendation
Wentao Shi, Xiangnan He*, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang & Fuli Feng SIGIR 2024 (Accept Rate: 20.1%) Codes *Corresponding |
Large Language-Recommendation Assistant
Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang & Xiangnan He* SIGIR 2024 (Accept Rate: 20.1%) Codes *Corresponding |
Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients
Zihao Zhao, Yi Jing, Fuli Feng, Jiancan Wu, Chongming Gao & Xiangnan He* SIGIR 2024 (Accept Rate: 20.1%) Codes *Corresponding |
Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
Tianhao Shi, Yang Zhang, Jizhi Zhang, Fuli Feng & Xiangnan He SIGIR 2024 (Accept Rate: 20.1%) Codes |
Diffusion Models for Generative Outfit Recommendation
Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma, Jizhi Zhang & Xiangnan He SIGIR 2024 (Accept Rate: 20.1%) Codes |
Item-side Fairness of Large Language Model-based Recommendation System
Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng & Xiangnan He* WWW 2024 (Accept Rate: 20.2%) Codes *Corresponding |
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
Wentao Shi, Chenxu Wang, Fuli Feng, Yang Zhang, Wenjie Wang, Junkang Wu & Xiangnan He* WWW 2024 (Accept Rate: 20.2%) Codes *Corresponding |
EXGC: Bridging Efficiency and Explainability in Graph Condensation
Junfeng Fang, Xinglin Li, Yongduo Sui, Yuan Gao, Guibin Zhang, Kun Wang, Xiang Wang & Xiangnan He WWW 2024 (Accept Rate: 20.2%) Codes |
Proactive Recommendation with Iterative Preference Guidance
Shuxian Bi, Wenjie Wang, Hang Pan, Fuli Feng & Xiangnan He WWW 2024 (Short) Codes |
Towards 3D Molecule-Text Interpretation in Language Models
Sihang Li, Zhiyuan Liu, Yanchen Luo, Xiang Wang, Xiangnan He*, Kenji Kawaguchi, Tat-Seng Chua & Qi Tian ICLR 2024 (Accept Rate: 31%) Codes *Corresponding |
Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference for Recommendation
Haoxuan Li, Chunyuan Zheng, Sihao Ding, Fuli Feng, Xiangnan He, Zhi Geng & Peng Wu ICLR 2024 (Accept Rate: 31%) |
Boosting Few-shot Learning via Attentive Feature Regularization
Xingyu Zhu, Shuo Wang, Jinda Lu, Yanbin Hao, Haifeng Liu & Xiangnan He AAAI 2024 (Accept Rate: 23.75%) |
Text-to-Image Generation for Abstract Concepts
Jiayi Liao, Xu Chen, Qiang Fu, Lun Du, Xiangnan He, Xiang Wang, Shi Han & Dongmei Zhang AAAI 2024 (Accept Rate: 23.75%) |
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
Chongming Gao, Wenqiang Lei, Jiawei Chen, Siqi Wang, Xiangnan He*, Shijun Li, Biao Li, Yuan Zhang & Peng Jiang ACM Transactions on Information Systems (TOIS) Codes *Corresponding |
Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach
Jinqiu Jin, Haoxuan Li, Fuli Feng, Sihao Ding, Peng Wu & Xiangnan He* NeurIPS 2023 (Accept Rate: 26.1%) Codes *Corresponding |
Understanding Contrastive Learning via Distributionally Robust Optimization
Junkang Wu, Jiawei Chen, Jiancan Wu, Wentao Shi, Xiang Wang & Xiangnan He* NeurIPS 2023 (Accept Rate: 26.1%) Codes *Corresponding |
Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis Junfeng Fang, Wei Liu, Xiang Wang, Zemin Liu, An Zhang, Yuan Gao & Xiangnan He* NeurIPS 2023 (Accept Rate: 26.1%) Codes *Corresponding |
Unleashing the Power of Graph Data Augmentation on Covariate Shift Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, JUN ZHOU, Xiang Wang & Xiangnan He* NeurIPS 2023 (Accept Rate: 26.1%) Codes *Corresponding |
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Zhengyi Yang, Jiancan Wu, Zhicai Wang, Xiang Wang, Yancheng Yuan & Xiangnan He
NeurIPS 2023 (Accept Rate: 26.1%) Codes |
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 & Peng Wu
NeurIPS 2023 (Accept Rate: 26.1%) |
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng & Xiangnan He* Recsys 2023 (Accept Rate: 25.3%) Codes *Corresponding |
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng & Xiangnan He* Recsys 2023 (Accept Rate: 25.3%) Codes *Corresponding |
RecAD: Towards A Unified Library for Recommender Attack and Defense
Changsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng & Xiangnan He Recsys 2023 (Reproducibility) Codes |
Semantic-based Selection, Synthesis, and Supervision for Few-shot Learning
Jinda Lu, Shuo Wang, XinYu Zhang, Yanbin Hao & Xiangnan He* ACM MM 2023 (Full, Accept Rate: 29.3%) Codes *Corresponding |
CgT-GAN: CLIP-guided Text GAN for Image Captioning
Jiarui Yu, Haoran Li, Yanbin Hao, Bin Zhu, Tong Xu & Xiangnan He* ACM MM 2023 (Full, Accept Rate: 29.3%) Codes *Corresponding |
Counterfactual Active Learning for Out-of-Distribution Generalization
Xun Deng, Wenjie Wang, Fuli Feng, Hanwang Zhang, Xiangnan He & Yong Liao ACL 2023 (Full) Codes |
Discriminative-Invariant Representation Learning for Unbiased Recommendation
Hang Pan, Jiawei Chen, Fuli Feng, Wentao Shi, Junkang Wu & Xiangnan He IJCAI 2023 (Full, Accept Rate: 15%) Codes |
A Generic Learning Framework for Sequential Recommendation with Distribution Shifts
Zhengyi Yang, Xiangnan He*, Jizhi Zhang, Jiancan Wu, Xin Xin, Jiawei Chen & Xiang Wang SIGIR 2023 (Full, Accept Rate: 20.1%) Codes *Corresponding |
Alleviating Matthew Effect of Offline Reinforcement Learning in Recommendation
Chongming Gao, Kexin Huang, Jiawei Chen, Yuan Zhang, Biao Li, Peng Jiang, Shiqi Wang, Zhong Zhang & Xiangnan He* SIGIR 2023 (Full, Accept Rate: 20.1%) Codes *Corresponding (Best Paper Honorable Mention) |
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, Accept Rate: 20.1%) Codes *Corresponding |
Diffusion Recommender Model
Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He & Tat-Seng Chua SIGIR 2023 (Full, Accept Rate: 20.1%) Codes |
Bi-directional Distribution Alignment for Transductive Zero Shot Learning
Zhicai Wang, Yanbin Hao, Tingting Mu, Ouxiang Li, Shuo Wang & Xiangnan He* CVPR 2023 (Full) Codes *Corresponding |
Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum
Yuan Gao, Xiang Wang, Xiangnan He*, Zhenguang Liu, Huamin Feng & Yongdong Zhang WWW 2023 (Full, Accept Rate: 19.2%) Codes *Corresponding |
On the Theories Behind Hard Negative Sampling for Recommendation
Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao & Xiangnan He* WWW 2023 (Full, Accept Rate: 19.2%) Codes *Corresponding |
GIF: A General Graph Unlearning Strategy via Influence Function
Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang & Xiangnan He* WWW 2023 (Full, Accept Rate: 19.2%) Codes *Corresponding |
Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendation
Jiawei Chen, Junkang Wu, Jiancan Wu, Xuezhi Cao, Sheng Zhou & Xiangnan He* WWW 2023 (Full, Accept Rate: 19.2%) Codes *Corresponding |
Knowledge Graph Embedding by Normalizing Flows
Changyi Xiao, Xiangnan He* & Yixin Cao AAAI 2023 (Full) Codes *Corresponding |
Unbiased Knowledge Distillation for Recommendation
Gang Chen, Jiawei Chen, Fuli Feng, Sheng Zhou & Xiangnan He* WSDM 2023 (Full, Accept Rate: 17.8%) Codes *Corresponding |
Alleviating Structural Distribution Shift in Graph Anomaly Detection
Yuan Gao, Xiang Wang, Xiangnan He*, Zhenguang Liu, Huamin Feng & Yongdong Zhang WSDM 2023 (Full, Accept Rate: 17.8%) Codes *Corresponding |
Cooperative Explanations of Graph Neural Networks
Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He & Tat-Seng Chua WSDM 2023 (Full, Accept Rate: 17.8%) Codes |
Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation
Chenxu Wang, Fuli Feng, Yang Zhang, Qifan Wang, Xunhan Hu & Xiangnan He* IEEE Transactions on Big Data (TBD 2023) Codes *Corresponding |
Learning to Double-check Model Prediction from a
Causal Perspective
Xun Deng, Fuli Feng, Xiang Wang, Xiangnan He, Hanwang Zhang & Tat-Seng Chua IEEE Transactions on Neural Networks and Learning Systems (TNNLS 2023) Codes |
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Zihao Zhao, Jiawei Chen, Sheng Zhou, Xiangnan He*, Xuezhi Cao, Fuzheng Zhang & Wei Wu IEEE Transactions on Knowledge and Data Engineering (TKDE 2023) Codes *Corresponding |
Causal Inference in Recommender Systems: A Survey and Future Directions
Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He & Yong Li ACM Transactions on Information Systems (TOIS 2023) |
Inductive Lottery Ticket Learning for Graph Neural Networks
Yongduo Sui, Xiang Wang, Tianlong Chen, Meng Wang, & Xiangnan He* & Tat-Seng Chua Journal of Computer Science and Technology (JCST) Codes *Corresponding |
Addressing Confounding Feature Issue for Causal Recommendation
Xiangnan He, Yang Zhang, Fuli Feng, Chonggang Song, Lingling Yi, Guohui Ling & Yongdong Zhang ACM Transactions on Information Systems (TOIS 2022) Codes |
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KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
Chongming Gao, Shijun Li, Wenqiang Lei, Jiawei Chen, Biao Li, Peng Jiang, Xiangnan He, Jiaxin Mao & Tat-Seng Chua CIKM 2022 (Full Research) Dataset Link a>, Codes a> |
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KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
Chongming Gao, Shijun Li, Yuan Zhang, Jiawei Chen, Biao Li, Wenqiang Lei, Peng Jiang & Xiangnan He CIKM 2022 (Resource Track) Dataset Link a> |
Multi-directional Knowledge Transfer for Few-Shot Learning
Shuo Wang, Xinyu Zhang, Yanbin Hao, Chengbing Wang & Xiangnan He* ACM MM 2022 (Full, Accept Rate: 27.9%) Codes *Corresponding |
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Hierarchical Hourglass Convolutional Network for Efficient Video Classification
Yi Tan, Yanbin Hao*, Hao Zhang, Shou Wang & Xiangnan He* ACM MM 2022 (Full, Accept Rate: 27.9%) Codes *Corresponding |
Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP
Zhicai Wang, Yanbin Hao, Xingyu Gao, Hao Zhang, Shuo Wang, Tingting Mu & Xiangnan He ACM MM 2022 (Full, Accept Rate: 27.9%) Codes |
Unsupervised Video Hashing with Multi-granularity Contextualization and Multi-structure Preservation
Yanbin Hao, Jingru Duan, Hao Zhang, Bin Zhu, Pengyuan Zhou & Xiangnan He ACM MM 2022 (Full, Accept Rate: 27.9%) Codes |
Invariant Representation Learning for Multimedia Recommendation
Xiaoyu Du, Zike Wu, Fuli Feng, Xiangnan He & Jinhui Tang ACM MM 2022 (Full, Accept Rate: 27.9%) Codes |
Causal Attention for Interpretable and Generalizable Graph Classification
Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He & Tat-Seng Chua KDD 2022 (Full Research, Accept Rate: 15%) Codes |
Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis
Sihao Ding, Peng Wu, Fuli Feng, Yitong Wang, Xiangnan He, Yong Liao & Yongdong Zhang KDD 2022 (Full Research, Accept Rate: 15%) Codes |
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Sihang Li, Xiang Wang*, An Zhang, Ying-Xin Wu, Xiangnan He* & Tat-Seng Chua ICML 2022 (Full, Accept Rate: 21.9%) Codes *Corresponding |
Reinforced Causal Explainer for Graph Neural Networks
Xiang Wang, Yingxin Wu, An Zhang, Fuli Feng, Xiangnan He & Tat-Seng Chua IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Codes |
Interpolative Distillation for Unifying Biased and Debiased Recommendation
Sihao Ding, Fuli Feng, Xiangnan He, Jinqiu Jin, Wenjie Wang, Yong Liao & Yongdong Zhang SIGIR 2022 (Full, Accept rate: 20%) Codes |
Group Contextualization for Video Recognition
Yanbin Hao, Hao Zhang, Chong-Wah Ngo & Xiangnan He CVPR 2022 (Full, Accept rate: 25.3%) Codes |
Discovering Invariant Rationales for Graph Neural Networks
Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He & Tat-Seng Chua ICLR 2022 Codes |
Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning
Moxin Li, Fuli Feng, Hanwang Zhang, Xiangnan He, Fengbin Zhu & Tat-Seng Chua ACL 2022 (Full, main conference) Codes |
Cross Pairwise Ranking for Unbiased Item Recommendation
Qi Wan, Xiangnan He*, Xiang Wang, Jiancan Wu, Wei Guo & Ruiming Tang WWW 2022 (Full, Accept rate: 17.7%) Codes *Corresponding author |
Causal Representation Learning for Out-of-Distribution Recommendation
Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin & Tat-Seng Chua WWW 2022 (Full, Accept rate: 17.7%) Codes |
Learning Robust Recommenders through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng, Jeoman Jose, Fuli Feng & Xiangnan He WWW 2022 (Full, Accept rate: 17.7%) Codes |
Interactive Hypergraph Neural Network for Personalized Product Search
Dian Cheng, Jiawei Chen, Wenjun Peng, Wenqin Ye, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng & Xiangnan He WWW 2022 (Full, Accept rate: 17.7%) Codes |
Bias and Debias in Recommender System: A Survey and Future Directions
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang & Xiangnan He* ACM Transactions on Information Systems (TOIS 2022) Slides *Corresponding |
Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He & Yong Li ACM Transactions on Recommender Systems (TORS 2022) |
Attention in Attention: Modeling Context Correlation for Efficient Video Classification
Yanbin Hao, Shuo Wang, Pei Cao, Xinjian Gao, Tong Xu, Jinmeng Wu & Xiangnan He IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) Codes |
Rumor Detection with Self-supervised Learning on Texts and Social Graph
Yuan Gao, Xiang Wang*, Xiangnan He*, Huamin Feng & Yongdong Zhang Frontiers of Computer Science (FOCS) *Corresponding |
Time-aware Path Reasoning on Knowledge Graph for Recommendation
Yuyue Zhao, Xiang Wang, Jiawei Chen, Wei Tang, Yashen Wang, Xiangnan He & Haiyong Xie ACM Transactions on Information Systems (TOIS 2022) Codes |
A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation
Le Wu, Xiangnan He, Xiang Wang, Kun Zhang & Meng Wang IEEE Transactions on Knowledge and Data Engineering (TKDE 2022) |
Causal Incremental Graph Convolution for Recommender System Retraining
Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi & Yongdong Zhang IEEE Transactions on Neural Networks and Learning Systems (TNNLS 2022) Codes |
GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
Jiajia Chen, Xin Xin, Xianfeng Liang, Xiangnan He & Jun Liu IEEE Transactions on Knowledge and Data Engineering (TKDE 2022) Codes |
CatGCN: Graph Convolutional Networks with Categorical Node Features
Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling & Yongdong Zhang IEEE Transactions on Knowledge and Data Engineering (TKDE 2022) Codes |
Exploring Lottery Ticket Hypothesis in Media Recommender Systems
Yanfang Wang, Yongduo Sui, Xiang Wang, Zhenguang Liu & Xiangnan He* International Journal of Intelligent Systems (IJIS 2022) Codes *Corresponding |
Graph Convolution Machine for Context-aware Recommender System
Jiancan Wu, Xiangnan He*, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian & Xing Xie Frontiers of Computer Science (FOCS 2022) Codes *Corresponding author |
Advances and Challenges in Conversational Recommender Systems: A Survey
Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke & Tat-Seng Chua AI Open Slides |
Towards Multi-Grained Explainability for Graph Neural Networks
Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He* & Tat-Seng Chua NeurIPS 2021 (Full, Accept rate: 26%) Codes Slides *Corresponding author |
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Junkang Wu, Wentao Shi, Xuezhi Cao, Jiawei Chen, Wenqiang Lei, Fuzheng Zhang, Wei Wu & Xiangnan He CIKM 2021 (Full, Accept rate: 21.3%) Codes Slides |
A Deep Learning Framework for Self-evolving Hierarchical Community Detection
Daizong Ding, Mi Zhang, Hanrui Wang, Xudong Pan, Min Yang & Xiangnan He CIKM 2021 (Full, Accept rate: 21.3%) Slides |
Selective Dependency Aggregation for Action Classification
Yi Tan, Yanbin Hao, Xiangnan He, Yinwei Wei & Xun Yang MM 2021 (Full, Accept rate: 28%) Codes Slides |
Causal Intervention for Leveraging Popularity Bias in Recommendation
Yang Zhang, Fuli Feng*, Xiangnan He*, Tianxin Wei, Chonggang Song, Guohui Ling & Yongdong Zhang SIGIR 2021 (Full, Accept rate: 21%) Codes Slides *Corresponding (Best Paper Honorable Mention) |
AutoDebias: Learning to Debias for Recommendation
Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He*, Xin Xin, Liang Chen, Guli Lin & Keping Yang SIGIR 2021 (Full, Accept rate: 21%) Codes Slides *Corresponding author |
Self-supervised Graph Learning for Recommendation
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian & Xing Xie SIGIR 2021 (Full, Accept rate: 21%) Codes Slides |
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang & Tat-Seng Chua SIGIR 2021 (Full, Accept rate: 21%) Codes Slides |
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
Fuli Feng, Weiran Huang, Xin Xin, Xiangnan He, Tat-Seng Chua & Qifan Wang SIGIR 2021 (Full, Accept rate: 21%) Codes Slides |
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
Tianxin Wei, Fuli Feng*, Jiawei Chen, Ziwei Wu, Jinfeng Yi & Xiangnan He* KDD 2021 (Full, Accept rate: 15.4%) Codes Slides *Corresponding author |
Deconfounded Recommendation for Alleviating Bias Amplification
Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang & Tat-Seng Chua KDD 2021 (Full, Accept rate: 15.4%) Codes Slides |
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
Hande Dong, Jiawei Chen, Fuli Feng, Xiangnan He, Shuxian Bi, Zhaolin Ding & Peng Cui WWW 2021 (Full, Accept rate: 20.6%) Codes Slides |
Disentangling User Interest and Conformity for Recommendation with Causal Embedding
Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li & Depeng Jin WWW 2021 (Full, Accept rate: 20.6%) Codes Slides |
Learning Intents behind Interactions with Knowledge Graph for Recommendation
Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He* & Tat-Seng Chua WWW 2021 (Full, Accept rate: 20.6%) Codes Slides *Corresponding author |
Denoising Implicit Feedback for Recommendation
Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie & Tat-Seng Chua WSDM 2021 (Full, Accept rate: 18.6%) Codes Slides |
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Shijun Li, Wenqiang Lei, Qingyun Wu, Xiangnan He, Peng Jiang & Tat-Seng Chua ACM Transactions on Information Systems (TOIS 2021) Codes |
Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions
Fuli Feng, Xiangnan He*, Hanwang Zhang & Tat-Seng Chua IEEE Transactions on Knowledge and Data Engineering (TKDE 2021) Codes *Corresponding author |
Adversarial Attack on Large Scale Graph
Jintang Li, Tao Xie, Liang Chen, Fenfang Xie, Xiangnan He & Zibin Zeng IEEE Transactions on Knowledge and Data Engineering (TKDE 2021) Codes |
Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
Shunyu Jiang, Fuli Feng, Weijian Chen, Xiang Li & Xiangnan He AI Open Codes |
Deep Learning for Matching in Search and Recommendation
Jun Xu, Xiangnan He & Hang Li Foundations and Trends in Information Retrieval (FNTIR 2020) Slides |
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang & Meng Wang SIGIR 2020 (Full, Accept rate: 26%) Codes Slides |
How to Retrain Recommender System? A Sequential Meta-Learning Approach
Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li & Yongdong Zhang SIGIR 2020 (Full, Accept rate: 26%) Codes Slides |
Bundle Recommendation with Graph Convolutional Networks
Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin & Yong Li SIGIR 2020 (Short) Codes (Best Short Paper Honorable Mention) |
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
Fajie Yuan, Xiangnan He, Alexandros Karatzoglou & Liguang Zhang SIGIR 2020 (Full, Accept rate: 26%) Codes |
Interactive Path Reasoning on Graph for Conversational Recommendation
Wenqiang Lei, Gangyi Zhang, Xiangnan He*, Yisong Miao, Xiang Wang, Liang Chen & Tat-Seng Chua KDD 2020 (Full, Accept rate: 16.9%) Codes Corresponding author |
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
Wenqiang Lei, Xiangnan He*, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan & Tat-Seng Chua WSDM 2020 (Full, Accept rate: 15%) Slides Data & Codes *Corresponding author |
Reinforced Negative Sampling over Knowledge Graph for Recommendation
Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Weng Wang & Tat-Seng Chua WWW 2020 (Full, Accept rate: 19%) Codes |
Future Data Helps Training: Modelling Future Contexts for Session-based Recommendation
Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu & Xiong Yilin WWW 2020 (Full, Accept rate: 19%) Codes Slides |
Bilinear Graph Neural Network with Neighbor Interactions
Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng & Yongdong Zhang IJCAI 2020 (Full, Accept rate: 12.6%) Codes |
Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning
Daizong Ding, Mi Zhang, Xudong Pan, Min Yang & Xiangnan He AAAI 2020 (Full, Accept rate: 20.6%) |
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Fuli Feng, Xiangnan He*, Jie Tang & Tat-Seng Chua IEEE Transactions on Knowledge and Data Engineering (TKDE 2020) Codes *Corresponding author |
Neural Graph Collaborative Filtering
Xiang Wang, Xiangnan He*, Meng Wang, Fuli Feng & Tat-Seng Chua SIGIR 2019 (Full, Accept rate: 20%) Slides Codes *Corresponding author |
Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation
Xin Xin, Xiangnan He*, Yongfeng Zhang, Yongdong Zhang & Joemon Jose SIGIR 2019 (Full, Accept rate: 20%) Slides Codes *Corresponding author |
KGAT: Knowledge Graph Attention Network for Recommendation
Xiang Wang, Xiangnan He*, Yixin Cao, Meng Liu & Tat-Seng Chua KDD 2019 (Full, Accept rate: 14.2%) Codes Poster*Corresponding author |
λOpt: Learn to Regularize Recommender Models in Finer Levels
Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou & Yue Wang KDD 2019 (Full Oral, Accept rate: 9.2%) Codes Slides Poster |
Modeling Extreme Events in Time Series Prediction
Daizong Ding, Mi Zhang, Xudong Pan, Min Yang & Xiangnan He KDD 2019 (Full Oral, Accept rate: 9.2%) Slides Poster |
Semi-supervised User Profiling with Heterogeneous Graph Attention Networks
Weijian Chen, Yulong Gu, Zhaochun Ren*, Xiangnan He*, Hongtao Xie, Tong Guo, Dawei Yin & Yongdong Zhang IJCAI 2019 (Full, Accept rate: 17.9%) Codes Slides *Corresponding author |
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preference
Yixin Cao, Xiang Wang, Xiangnan He*, Zikun Hu & Tat-Seng Chua WWW 2019 (Full, Accept rate: 18%) Codes *Corresponding author |
Explainable Reasoning over Knowledge Graph Paths for Recommendation
Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao & Tat-Seng Chua AAAI 2019 (Full, Accept rate: 16.2%) Codes Slides |
A Simple Convolutional Generative Network for Next-item Recommendation
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose & Xiangnan He WSDM 2019 (Full, Accept rate: 16.4%) Codes |
Fast Matrix Factorization with Non-Uniform Weights on Missing Data
Xiangnan He, Jinhui Tang, Xiaoyu Du, Richang Hong, Tongwei Ren & Tat-Seng Chua IEEE Transactions on Neural Networks and Learning Systems (TNNLS 2019, IF=7.982) Codes |
Adversarial Personalized Ranking for Recommendation
Xiangnan He, Zhankui He, Xiaoyu Du & Tat-Seng Chua SIGIR 2018 (Full, Accept rate: 21%) Codes Slides |
Knowledge-aware Multimodal Dialog Systems
Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong & Tat-Seng Chua MM 2018 (Full, oral, Accept rate: 8.5%) Slides (Best Paper Final List) |
Aesthetic-based Clothing Recommendation
Wenhui Yu, Huidi Zhang, Xiangnan He, Xu Chen, Li Xiong & Zheng Qin WWW 2018 (Accept rate: 14.8%) Slides Codes (Best Paper Award Honorable Mention) |
TEM: Tree-enhanced Embedding Model for Explainable Recommendation
Xiang Wang, Xiangnan He*, Fuli Feng, Liqiang Nie & Tat-Seng Chua WWW 2018 (Accept rate: 14.8%) Codes are under request. Slides *Corresponding author. |
Learning on Partial-Order Hypergraphs
Fuli Feng, Xiangnan He*, Yiqun Liu, Liqiang Nie & Tat-Seng Chua WWW 2018 (Accept rate: 14.8%) Codes are under request. Slides *Corresponding author. |
An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data
Jingtao Ding, Fuli Feng, Xiangnan He, Guanghui Yu, Yong Li & Depeng Jin WWW 2018 (Poster) Codes (Best Poster Award) |
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures
Wenqiang Lei, Xisen Jin, Min-Yen Kan, Zhaochun Ren, Xiangnan He & Dawei Yin. ACL 2018 (Full) Codes Slides |
Attributed Social Network Embedding
Lizi Liao, Xiangnan He*, Hanwang Zhang, & Tat-Seng Chua IEEE Transactions on Knowledge and Data Engineering (TKDE) Codes *Corresponding author |
NAIS: Neural Attentive Item Similarity Model for Recommendation
Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, & Tat-Seng Chua IEEE Transactions on Knowledge and Data Engineering (TKDE) Codes |
Neural Factorization Machines for Sparse Predictive Analytics
Xiangnan He & Tat-Seng Chua SIGIR 2017 (Accept rate: 22%) Codes Slides |
Item Silk Road: Recommending Items from Information Domains to Social Users
Xiang Wang, Xiangnan He*, Liqiang Nie & Tat-Seng Chua SIGIR 2017 (Accept rate: 22%) Codes Slides *Corresponding author |
Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-level Attention
Jingyuan Chen, Hanwang Zhang, Xiangnan He*, Liqiang Nie, Wei Liu & Tat-Seng Chua SIGIR 2017 (Accept rate: 22%) Codes Slides *Corresponding author |
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Jun Xiao, Hao Ye, Xiangnan He*, Hanwang Zhang, Fei Wu & Tat-Seng Chua IJCAI 2017 (Accept rate: 26%) Codes Slides *Corresponding author |
Neural Collaborative Filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu & Tat-Seng Chua WWW 2017 (Accept rate: 17%) Codes Slides |
A Generic Coordinate Descent Framework for Learning from Implicit Feedback
Immanuel Bayer*, Xiangnan He*, Bhargav Kanagal & Steffen Rendle WWW 2017 (Accept rate: 17%) *Joint work at Google. |
BiRank: Towards Ranking on Bipartite Graphs
Xiangnan He, Ming Gao, Min-Yen Kan & Dingxian Wang IEEE Transactions on Knowledge and Data Engineering (TKDE) |
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
Xiangnan He, Hanwang Zhang, Min-Yen Kan & Tat-Seng Chua SIGIR 2016. (Accept rate: 18%) Codes Slides |
Discrete Collaborative Filtering
Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan & Chua Tat-Seng SIGIR 2016. (Accept rate: 18%) Codes Slides (Best Paper Award Honorable Mention) |
Context-aware Image Tweets Modelling and Recommendation
Tao Chen, Xiangnan He & Min-Yen Kan MM 2016. (Accept rate: 20%) Codes |
TriRank: Review-aware Explainable Recommendation by Modeling Aspects
Xiangnan He, Tao Chen, Min-Yen Kan & Xiao Chen CIKM 2015. (Accept rate: 18%) Slides |
Relating an Image Tweet’s Text and Images
Tao Chen, Hany M. SalahEldeen, Xiangnan He, Min-Yen Kan & Dongyuan Lu AAAI 2015. (Accept rate: 26.7%) Codes |
Predicting the Popularity of Web 2.0 Items Based on User Comments
Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu & Kazunari Sugiyama SIGIR 2014. (Accept rate: 21%) Slides |
Comment-based Multi-View Clustering of Web 2.0 Items
Xiangnan He, Min-Yen Kan, Peichu Xie & Xiao Chen WWW 2014. (Accept rate: 12.9%) Supplement Slides Codes |
Professional Services
Journal Editorial Board / Associate Editor: IEEE Transactions on Knowledge and Data Engineering (TKDE) --- The flagship journal in data engineering ACM Transactions on Recommender Systems (TORS) --- The flagship journal in recommender systems IEEE Transactions on Big Data (TBD) --- The flagship journal in big data ACM Transactions on Information Systems (TOIS) --- The flagship journal in information retrieval Foundations and Trends in Information Retrieval (FnTIR) --- For survey and tutorial articles on IR topics AI Open --- A new AI journal for open and sharing Social Network Analysis and Mining --- A leading journal in the field of network analysis and mining |
Conference/Workshop Organization: Demo Chair of WWW 2024 Senior Area Chair of LREC-COLING 2024 Program Committee Chair of SMP 2023 Workshop Chair of SIGIR-AP 2023 Doctoral Consortium Chair of Recsys 2023 Short Paper Chair of SIGIR 2023 Sponsorship Chair of WSDM 2023 Area Chair of EMNLP 2022 Doctoral Consortium Chair of WSDM 2020 Area Chair (Multimedia Search and Recommendation) of ACM Multimedia 2020 Applied Data Science Chair of ECML-PKDD 2020 Workshop Co-Chair of the SIGIR 2020 Workshop on Information Retrieval in Finance (FinIR) Area Chair (NLP Applications) of CCF NLPCC 2020 Special Session Chair of ACM Multimedia Asia 2020 Youth Forum Chair of China Conference on Information Retrieval (CCIR 2020) Program Chair of IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS 2019) Area Chair (Multimedia Search and Recommendation) of ACM Multimedia 2019 Workshop Co-Chair of the CIKM 2017 Workshop on Social Media Analytics for Smart Cities |
PC/SPC Member of Conferences: 2024: SIGIR, WWW, WSDM, AAAI, ICLR, CVPR, IJCAI, Recsys 2023: SIGIR, WWW, NeurIPS, MM, WSDM, Recsys, AAAI, IJCAI, ICLR, ICML, CVPR 2022: SIGIR, WWW, KDD, NeurIPS, ICML, WSDM, AAAI, IJCAI, CVPR 2021: SIGIR, WWW, KDD, NeurIPS, ICML, WSDM, CIKM, AAAI, IJCAI 2020: SIGIR, WWW, KDD, NeurIPS, WSDM, CIKM, AAAI, IJCAI, EMNLP, ICTIR 2019: SIGIR, WWW, KDD, WSDM, CIKM, AAAI, IJCAI, ACL, ICME 2018: SIGIR, WWW, KDD, WSDM, CIKM, AAAI, IJCAI, EMNLP, ACL, MM, AIRS, PAKDD 2017: SIGIR, WWW, CIKM, MM 2016: SIGIR, WWW, EMNLP |
Experiences
Professor, University of Science and Technology of China, March 2019 - Present |
Postdoc Research Fellow, National University of Singapore, May 2016 - March 2019 Advisior: Chua Tat-Seng (NExT: NUS-Tsinghua Extreme Search Center) |
Research Intern, Google Research (Mountain View), June 2015 - Sep 2015 Advisior: Bhargav Kanagal and Steffen Rendle (Strategic Technology Group) |
Software Engineering Intern, Google (New York), June 2014 - Sep 2014 Advisior: Thomas Sidoti and Sheng Zhang (Local Search Group) |
Software Engineering Intern, Microsoft (Shanghai), Sep 2010 - Dec 2010 Advisior: Leon Chen (Operating System Group) |
Education
National University of Singapore (NUS) Ph.D. in Computer Science July 2011 - April 2016, Singapore Advisor: Prof. Kan Min-Yen |
East China Normal University (ECNU) Bachelor in Software Engineering Sep 2007 - June 2011, Shanghai Advisor: Prof. Jin Cheqing and Prof. Zhou Aoying |
Useful Links
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Last update: 1 Oct, 2024. Webpage template borrows from Weinan Zhang.