News
26 Mar. 2024
Two full paper are accepted by SIGIR'24, on Graph-based Recommendation and Interative Recommendation.
23 Jan. 2024
One full paper is accepted by WWW'24, on Robust Recommendation.
1 Dec. 2023
One full paper is accepted by TOIS, on Recommendation Loss.
1 Dec. 2023
Two full papers are accepted by ICDE'24, on Graph Quries and Recommendation Loss.
22 Sep. 2023
Two full papers are accepted by NIPS'23, on Constrastive Learning and Graph Structure Learning.
5 Aug. 2023
Two full papers are accepted by CIKM'23, on Recommendation Debiasing and Graph Structure Learning.
25 July. 2023
Our SIGIR'23 paper "Alleviating Matthew Effect of Offline Reinforcement Learning in Recommendation" receives Best Paper Honorable Mention .
20 Apr. 2023
One full paper is accepted by IJCAI'23, on Recommendation Debiasing.
08 Apr. 2023
One paper is accepted by TOIS'23, on Causal Interactive Rec .
05 Apr. 2023
Two full papers are accepted by SIGIR'23, on Debiasing Interactive Rec and Robust Sequential Rec.
26 Jan. 2023
Two full papers are accepted by WWW'23, on Adaptive Temperature and Negative Sampling in Recsys.
31 Oct. 2022
One full paper is accepted by AAAI'23, on Information Propagation .
31 Oct. 2022
One full paper is accepted by TKDE, on Dynamic Popularity Bias.
18 Oct. 2022
One full paper is accepted by WSDM’23, on Fair Knowledge Distillation.
15 Aug. 2022
Two papers are accepted by CIKM’22, on Recommendation Datasets.
15 Aug. 2022
One survey paper is accepted by TOIS, on Recommendation Debiasing.
15 Jan. 2022
One full paper is accepted by WWW'22, on Hypergraph-based Personalized Search.
29 Dec. 2021
We release a new version of the survey on recommendation debiasing, with supplementing causal explanations of biases and the latest debiasing technologies.
8 Aug. 2021
One full paper is accepted by CIKM'21, on disentangled knowledge graph representation learning.
27 July. 2021
One full paper is accepted by ICCV'21, on GCN-based knowledge distillation.
27 July. 2021
One full paper is accepted by TKDE, on sampling for recommendation.
12 June. 2021
Our tutorial on Bias and debias in recommendation is also accepted by Recsys'21.
18 May 2021
One full paper is accepted by KDD'21, on causal inference for recommendation debiasing.
15 April 2021
One full paper is accepted by SIGIR'21, on universal debiasing framework for recommendation .
7 Feb. 2021
One full paper is accepted by TOIS'21, on Sampling for recommendation .
15 Jan. 2021
One full paper is accepted by WWW'21, on Equivalence of GCN and label propagation .
15 Dec. 2020
One tutorial is accepted by WWW'21, on Bias and debias in recommendation.
11 Nov. 2019
Two full papers are accepted by AAAI, on Unbiased recommendation and Graph embedding.
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Jiawei Chen
"Hundred Talent" Research Fellow
College of Computer Science and Technology
38 Zheda Road, Hangzhou, China 230027
Email: sleepyhunt AT zju.edu.cn
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I am a "Hundred Talent" Research Fellow in College of Computer Science and Technology, Zhejiang University. I have over 30 publications appeared in top conferences and journals such as AAAI, WWW, SIGIR, KDD, TOIS and CIKM. My research interests include information retrieval, data mining and machine learning, particularly in recommender systems, robustness, and graph neural network. Moreover, I have served as the PC member for top-tier conferences including AAAI, WWW, KDD, ACMMM and the invited reviewer for prestigious journals such as TNNLS, TKDE, TOIS.
Notes: 1. We recently release two surveys on recommendation biases and deep clustering:
- Bias and Debias in Recommender System: A Survey and Future Directions
- A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions
- You may be interested in these emerging topics!
2. I'm looking for self-motivated master/PHD students. If you are interested in the recommender system, large language models, graph mining, diffusion model or robust machine learning, please send me your CV.
- Bias and Debias in Recommender System: A Survey and Future Directions
- A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions
- You may be interested in these emerging topics!
2. I'm looking for self-motivated master/PHD students. If you are interested in the recommender system, large language models, graph mining, diffusion model or robust machine learning, please send me your CV.
Tutorial
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Bias Issues and Solutions in Recommender System
Jiawei Chen, Xiang Wang, Fuli Feng, Xiangnan He WWW'21, RecSys'21 Slide |
Selected Publications
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EasyRL4Rec: A User-Friendly Code Library for Reinforcement Learning Based Recommender Systems
Yuanqing Yu, Chongming Gao, Jiawei Chen*, Heng Tang, Yuefeng Sun, Qian Chen, Weizhi Ma, Min Zhang SIGIR'24 (Full Paper) , *Corresponding, |
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SIGformer: Sign-aware Graph Transformer for Recommendation
Sirui Chen, Jiawei Chen*, Sheng Zhou, Bohao Wang, Shen Han, Chanfei Su, Yuqing Yuan, Can Wang SIGIR'24 (Full Paper) , *Corresponding, |
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Distributionally Robust Graph-based Recommendation System
Bohao Wang, Jiawei Chen*, Changdong Li, Sheng Zhou, Qihao Shi, Yang Gao, Yan Feng, Chun Chen, Can Wang WWW'24 (Full Paper) , *Corresponding, |
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BSL: Understanding and Improving Softmax Loss for Recommendation
Junkang Wu, Jiawei Chen*, Jiancan Wu, Wentao Shi, Jizhi Zhang, Xiang Wang ICDE'24 (Full Paper) , *Corresponding, |
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Understanding Contrastive Learning via Distributionally Robust Optimization
Junkang Wu, Jiawei Chen*, Jiancan Wu, Wentao Shi, Xiang Wang, Xiangnan He* NIPS'23 (Full Paper) , *Corresponding, |
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CDR: Conservative Doubly Robust Learning for Debiased Recommendation
Zijie Song, Jiawei Chen*, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen, Can Wang CIKM'23 (Full Paper) , *Corresponding, |
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Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Chongming Gao, Kexin Huang, Jiawei Chen*, Yuan Zhang, Biao Li, Peng Jiang, Shiqi Wang, Zhong Zhang & Xiangnan He* SIGIR'23 (Full Paper, Accept Rate: 20.1%) , *Corresponding (Best Paper Honorable Mention) |
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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'23 (Full Paper, Accept Rate: 20.1%) , |
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Discriminative-Invariant Representation Learning for Unbiased Recommendation
Hang Pan, Jiawei Chen*, Fuli Feng, Wentao Shi, Junkang Wu & Xiangnan He IJCAI'23 (Full, Accept Rate: 15%) , *Corresponding |
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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 & Xiangnan He* WWW'23(Full Paper, Accept rate: 19.2%), |
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Adap-tau: Adaptively Modulating Embedding Magnitude for Recommendation
Jiawei Chen, Junkang Wu, Jiancan Wu, Sheng Zhou, Xuezhi Cao, Xiangnan He WWW'23(Full Paper, Accept rate: 19.2%), |
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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 TKDE'23, *Corresponding author |
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CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
Chongming Gao, Shiqi Wang, Shijun Li, Jiawei Chen*, Xiangnan He*, Wenqiang Lei, Biao Li, Yuan Zhang, Peng Jiang TOIS'23,*Corresponding author |
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Unbiased Knowledge Distillation for Recommendation
Gang Chen, Jiawei Chen*, Fuli Feng, Sheng Zhou, Xiangnan He* WSDM'23 (Full Paper, Accept rate: 17.8%), *Corresponding author |
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KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
Chongming Gao, Shijun Li, Wenqiang Lei, Biao Li, Peng Jiang, Jiawei Chen, Xiangnan He, Jiaxin Mao, Tat-Seng Chua CIKM'22 (Full Paper) |
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KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
Chongming Gao, Shijun Li, Yuan Zhang, Jiawei Chen, Biao Li, Xiangnan He, Wenqiang Lei, Peng Jiang CIKM'22 (Resource Track) |
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Bias and Debias in Recommender System: A Survey and Future Directions
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, Xiangnan He TOIS'22 |
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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%), *Corresponding author |
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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: 21.7%), *Corresponding author |
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SamWalker++: Recommendation with Informative Sampling Strategy
Can Wang, Jiawei Chen*, Sheng Zhou, Qihao Shi, Yan Feng and Chun Chen TKDE'21, *Corresponding author |
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Distilling Holistic Knowledge with Graph Neural Networks
Sheng Zhou, Yuchen Wang, Defang Chen, Jiawei Chen, Xin Wang, Can Wang, Jiajun Bu ICCV'21 (Full, Accept rate: 25.9%) |
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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 & Xiangnan He KDD'2021 (Full, Accept rate: 15.4%) |
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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%) |
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CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
Jiawei Chen, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, Xiangnan He TOIS'2021 |
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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, Peng Cui WWW'2021 (Full, Oral, Accept rate: 20.6% ), *Corresponding author |
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Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen, Yan Feng, Chun Chen AAAI 2020 (Full, Oral, Accept rate: 20.6%) |
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DGE: Deep Generative Network Embedding Based on Commonality and Individuality
Sheng Zhou, Xin Wang, Jiajun Bu, Martin Ester, Pinggang Yu, Jiawei Chen , Qihao Shi, Can Wang AAAI 2020 (Full, Poster, Accept rate: 20.6%) |
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Samwalker: Social recommendation with informative sampling strategy
Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen WWW 2019 (Full, Oral, Accept rate: 18%) |
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Adaptive Influence Blocking: Minimizing the Negative Spread by Observation-Based Policies
Qihao Shi, Can Wang, Deshi Ye, Jiawei Chen, Yan Feng, Chun Chen ICDE 2019 (Full, Oral) |
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Social recommendation with missing not at random data
Jiawei Chen, Can Wang, Martin Ester, Qihao Shi, Yan Feng, Chun Chen ICDM 2018 (Full, Oral, Accept rate: 8.86%) |
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Modeling Users' Exposure with Social Knowledge Influence and Consumption Influence for Recommendation
Jiawei Chen, Yan Feng, Martin Ester, Sheng Zhou, Chun Chen, Can Wang CIKM 2018 (Full, Oral, Accept rate: 17%) |
Professional Services
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PC/SPC Member of Conferences: (Senior) Program Committee Member of IJCAI (2021,2022) Program Committee Member of WWW (2021,2022,2023) Program Committee Member of AAAI (2021,2022,2023) Program Committee Member of KDD (2021,2022) Program Committee Member of ACMMM (2020,2021,2022) Program Committee Member of ECML-PKDD (2020,2021) Invited Reviewer for IEEE Transactions on Neural Networks and Learning Systems (TNNLS) Invited Reviewer for IEEE Transactions on Information system (TOIS) Invited Reviewer for IEEE Transactions on Knowledge and Data Engineering (TKDE) |
Experiences
| Postdoc Research Fellow, University of Science and Technology of China Advisor: Prof. Xiangnan He, July 2020 - July 2022 |
Education
| Zhejiang University (ZJU) Ph.D. in Computer Science Feb. 2017 - June 2020, Hangzhou Advisor: Prof. Chun Chen Master in Computer Science Sep. 2014 - Feb.2017, Hangzhou Advisor: Prof. Chun Chen |
| University of Electronic Science and Technology of China (UESTC) Bachelor in Micro-electronic Sep 2010 - June 2014, Chengdu |
Useful Links
| Machine Learning Reading List |
| Deep Learning Reading List |
Last update: Oct. 20, 2022. Webpage template borrows from Xiangnan He.