News
07 Oct 2024
One paper was accepted by Communications of the ACM (CACM) on recommendation for LLM-based agent platform.
23 Sep 2024
One paper was accepted by EMNLP 2024 on LLM-based recommendation, and one paper was accepted by ACM TORS on recommendation unlearning.
16 July 2024
One short paper is accepted by CIKM 2024 on LLM-based recommendation.
17 May 2024
One paper is accepted by KDD 2024 on recommendation multi-task learning.
16 May 2024
Two papers are accepted by ACL 2024 on LLM-based Recommendation and evaluation for LLM.
26 Mar 2024
Two papers are accepted by SIGIR 2024 on recommendation fariness and LLM-based Recommendation.
3 Mar 2024
One papers is accepted by TKDE on causal recommendation.
23 Jan 2024
One paper is accepted by WWW 2024 on top-K recommendation metric/loss.
20 Oct 2023
One paper is accepted by WSDM 2024 about label learning for short-video recommendation.
21 Sep 2023
I received RecSys 2023 Outstanding Reviewer Award.
6 Aug 2023
One paper is accepted by CIKM 2023 about label debiasing for recommendation.
27 July 2023
One co-authored paper is accepted by IEEE TBD about Uncertainty-aware Recommendation.
28 June 2023
Two co-authored short papers are accepted by RecSys 2023 about LLM for Recommendation.
5 April 2023
One full paper, one short paper and one DC paper have been accepted by SIGIR 2023 on invariant learning and abductive reasoning for recommendation.
26 Feb 2023
One paper is accepted by ICDE 2023 on invariant learning for loan default prediction.
30 July 2022
One paper is accepted by ACM TOIS on causal recommendation.
14 July 2021
Our SIGIR'21 paper "Causal Intervention for Leveraging Popularity Bias in Recommendation" receives Best Paper Honorable Mention.
16 April 2021
One first-author full paper is accepted by SIGIR, on causal inference for popularity bias in recsys. Thanks for my advisor and other co-authors!
23 April 2020
One first-author full paper is accepted by SIGIR, on meta-learning for recsys. Thanks for all co-authors!
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Yang Zhang
NExT++ Research Center • GitHub • Google Scholar
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I am a postdoctoral research fellow, working with Prof. Tat-seng Chua at National University of Singapore. I obtained my PhD from the University of Science and Technology of China in March 2024, supervised by Prof. Xiangnan He. Prior to that, I received my B.Eng. degree from USTC in 2019. My recent research focuses on recommendation systems and LLM/Agent personalization. I have authored over ten publications featured in top conferences and journals such as SIGIR, ACL, and TOIS. Notably, my research on causal debiasing earned the distinction of Best Paper Honorable Mention at SIGIR 2021. Additionally, I have served as a Program Committee (PC) member and reviewer for esteemed conferences and journals, including TOIS, TKDE, TIST, TKDD, TNNLS, WSDM, SIGIR, RecSys, SIGKDD, AAAI, etc.
In the Year of 2023:
Before 2022:
Education
| University of Sicence and Technology of China (USTC) PhD student in Information and Communication Engineering Sep 2019 - Mar 2024, Hefei, China Advisor: Prof. Xiangnan He Mentor: Prof. Fuli Feng |
| University of Sicence and Technology of China (USTC) Bachelor in Electroic Information Engineering Sep 2015 - June 2019, Hefei, China |
Experiences
| Research Intern, Wechat, Tencent (Shen Zhen), July 2020 - July 2021 Mentor: Dr. Chonggang song (Wechat, Tencent) |
Tutorials & Workshop
| 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 & SIGIR 2024 web page |
| The 1st Workshop on Recommendation with Generative Models
Wenjie Wang, Yong Liu, Yang Zhang, Weiwen Liu, Fuli Feng, Xiangnan He, Aixin Sun CIKM 2023 & WWW 2024 web page |
| Causal Recommendation: Progresses and Future Directions
Wenjie Wang, Yang Zhang, Haoxuan Li, Peng Wu, Fuli Feng, Xiangnan He SIGIR 2023 web page |
| Causal Recommendation: Progresses and Future Directions
Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, Xiangnan He WWW 2022 web page |
Preprints
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CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation
Yang Zhang, Fuli Feng, Jizhi Zhang, Keqin Bao, Qifan Wang and Xiangnan He ArXiv 2023. Codes |
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Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendation
Yang Zhang, Juntao You, Yimeng Bai, Jizhi Zhang, Keqin Bao, Wenjie Wang, Tat-seng Chua ArXiv 2024. Codes |
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Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning
Keqin Bao, Ming Yan, Yang Zhang, Jizhi Zhang, Wenjie Wang, Fuli Feng, Xiangnan He ArXiv 2024. Codes |
Publications
In the Year of 2024: |
Text-like Encoding of Collaborative Information in Large Language Models for Recommendation
Yang Zhang, Keqin Bao, Ming Yan, Wenjie Wang, Fuli Feng, Xiangnan He ACL 2024 (Main, Long) Codes |
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Recommendation Unlearning via Influence Function
Yang Zhang, Zhiyu Hu, Yimeng Bai, Jiancan Wu, Qifan Wang, Fuli Feng ACM TORS (Original Research) Codes |
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Envision Recommendation on LLM-based Agent Platform
Jizhi Zhang, Keqin Bao, Wenjie Wang, Yang Zhang, Wentao Shi, Wanhong Xu, Fuli Feng, Tat-Seng Chua CACM 2024. pdf |
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Decoding Matters: Addressing Amplification Bias and Homogeneity Issue in Recommendations for Large Language Models
Keqin Bao, Jizhi Zhang, Yang Zhang*, Xinyue Huo, Chong Chen, Fuli Feng* EMNLP 2024 (Main, Long) Codes *Corresponding |
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Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems
Tianhao Shi, Yang Zhang*, Zhijian Xu, Chong Chen, Fuli Feng, Xiangnan He, Qi Tian CIKM 2024 (Short) Codes *Corresponding |
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GradCraft: Elevating Multi-task Recommendations through Holistic Gradient Crafting
Yimeng Bai, Yang Zhang*, Fuli Feng*, Jing Lu, Xiaoxue Zang, Chenyi Lei, Yang Song KDD 2024 (ADS, AR: 20%) *Corresponding |
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Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
Tianhao Shi, Yang Zhang*, Jizhi Zhang, Fuli Feng* & Xiangnan He SIGIR 2024 (Full, AR: 20.1%) Codes *Corresponding |
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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 2024 (Poster, AR: 18%). Codes *Corresponding |
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Mitigating Hidden Confounding Effects for Causal Recommendation
Xinyuan Zhu, Yang Zhang*, Fuli Feng*, Xun Yang, Dingxian Wang, Xiangnan He TKDE 2024 (Regular) *Corresponding |
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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 (Full, AR: 20.1%) Codes |
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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 (Research, AR: 20.2%) Codes |
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Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction
Xiaoyuan Li, Wenjie Wang, Moxin Li, Junrong Guo, Yang Zhang, Fuli Feng ACL 2024 (Findings) |
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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, Yang Song CIKM 2023 (Applied Research Paper, AR: 32%). pdf |
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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 and Yongdong Zhang SIGIR 2023 (Full, AR: 20%) pdf Codes |
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Addressing Confounding Feature Issue for Causal Recommendation
Xiangnan He, Yang Zhang*, Fuli Feng, Chonggang Song, Lingling Yi, Guohui Ling & Yongdong Zhang ACM TOIS 2023 pdf Codes *Corresponding |
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Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation
Yulong Huang*, Yang Zhang*, Qifan Wang, Chenxu Wang and Fuli Feng SIGIR 2023 (short, AR: 25%) pdf Codes *Equal contribution |
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LightMIRM: Light Meta-learned Invariant Risk Minimization for Trustworthy Loan Default Prediction
Meng Jiang, Yang Zhang, Yuan Gao, Yansong Wang, Fuli Feng and Xiangnan He ICDE 2023 (Industry and Applications Track) pdf Codes |
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Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation
Chenxu Wang, Fuli Feng, Yang Zhang, Qifan Wang, Xunhan Hu, Xiangnan He IEEE TBD 2023; pdf |
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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 (Short). pdf |
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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 (Short). pdf |
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Mitigating Spurious Correlations for Self-supervised Recommendation
Xinyu Lin, Yiyan Xu, Wenjie Wang, Yang Zhang and Fuli Feng Machine Intelligence Research (2023) pdf Codes |
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Towards Trustworthy Recommender System: A Faithful and Responsible Recommendation Perspective
Yang Zhang SIGIR 2023 (Doctoral Consortium) pdf |
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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, AR: 21%) pdf Codes; Best Paper Honorable Mention |
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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 & Yongdong Zhang SIGIR 2020 (Full, AR: 26%) pdf Codes |
Services & Awards & Patents
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Invited Reviewer for ACM TOIS, IEEE TKDE, IEEE TNNLS, ACM TIST and ACM TKDD |
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Program committee member of WWW (2025, 2024), SIGIR (2024,2023), KDD (2025,2024,2023), RecSys (2024,2023), WSDM (2025,2024,2023), CIKM 2024, ICLR 2025, AAAI (2025, 2024,2023), SIGIR-AP (2023), ECML/PKDD 2022 |
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RecSys 2023 Outstanding Reviewer Award, 2023
- ACM RecSys |
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National Scholarship, 2021
- USTC, China |
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SIGIR Best Paper Honorable Mention, 2021
- ACM SIGIR |
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National Scholarship, 2018
- USTC, China |
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National Encouragement Scholarship, 2017
- USTC, China |
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Outstanding Student Scholarship, 2016&2017
- USTC, China |
Useful Links
| Prof. Xiangnan He | Causality Reading List | |
Last update: May 19, 2024. Webpage template borrows from prof. Xiangnan He.