Yaochen Zhu (朱耀晨)

Publications

Surveys:

1. Knowledge Editing for Large Language Models: A Survey
   Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, Jundong Li
   ACM CSUR 2024 | paper

Conference Papers:

1. LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience
   Yaochen Zhu, Harald Steck, Dawen Liang, Yinhan He, Nathan Kallus, Jundong Li
   EMNLP 2025 (Findings) | paper | codes
   Best paper award in KDD 2025 GenAIRecP workshop [Certificate]
2. Learning from Diverse Reasoning Paths with Routing and Collaboration
   Zhenyu Lei, Zhen Tan, Song Wang, Yaochen Zhu, Zihan Chen, Yushun Dong, Jundong Li
   EMNLP 2025 (Main) | paper | codes
3. CoRAG: Enhancing Hybrid Retrieval-Augmented Generation through a Cooperative Retriever Architecture
   Zaiyi Zheng, Song Wang, Zihan Chen, Yaochen Zhu, Yinhan He, Liangjie Hong, Qi Guo, Jundong Li
   EMNLP 2025 (Findings) | paper | codes
4. MI4Rec: Pretrained Language Model based Cold-Start Recommendation with Meta-Item Embeddings
   Zaiyi Zheng, Yaochen Zhu, Haochen Liu, Mingxuan Ju, Tong Zhao, Neil Shah, Jundong Li
   CIKM 2025 | paper | codes
Work done in collaboration with Snap Research
5. Towards Global-level Mechanistic Interpretability: A Perspective of Modular Circuits of Large Language Models
   Yinhan He, Wendy Zheng, Yushun Dong, Yaochen Zhu, Chen Chen, Jundong Li
   ICML 2025 | paper | codes
6. Causal Effect Estimation with Mixed Latent Confounders and Post-treatment Variables
   Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
   ICLR 2025 | paper | codes
7. Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems
   Yaochen Zhu, Chao Wan, Harald Steck, Dawen Liang, Yesu Feng, Nathan Kallus, Jundong Li
   WWW 2025 | paper | codes
   Work done when Yaochen was a machine learning research intern at Netflix Inc. (Summer'24)
8. Collaborative Diffusion Model for Recommender System
   Gyuseok Lee, Yaochen Zhu, Hwanjo Yu, Yao Zhou, Jundong Li
   WWW 2025 (Short Paper) | paper | codes
   Work done when Gyuseok was a research intern at UVA with me.
9. Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective
   Yinhan He, Zaiyi Zheng, Patrick Soga, Yaochen Zhu, Yushun Dong, Jundong Li
   EMNLP 2024 (Findings) | paper | codes
10. Understanding and Modeling Job Marketplace with Pretrained Language Models
    Yaochen Zhu, Liang Wu, Binchi Zhang, Song Wang, Qi Guo, Liangjie Hong, Luke Simon, Jundong Li
    CIKM 2024 | paper
11. Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations
    Linxin Guo, Yaochen Zhu, Min Gao, Yinghui Tao, Junliang Yu, Jundong Li
    KDD 2024 | paper | codes
12. Knowledge Graph-Enhanced Large Language Models via Path Selection
    Haochen Liu, Song Wang, Yaochen Zhu, Yushun Dong, Jundong Li
    ACL 2024 (Findings) | paper | codes
13. Collaborative Large Language Model for Recommender Systems
    Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
    WWW 2024 | paper | codes
    Invited Poster Presentation at 2024 Netflix Workshop on Personalization, Recommendation and Search (PRS).
    Work done when Yaochen was an applied research intern at Linkedin Inc (Summer'23).
14. Path-specific Counterfactual Fairness for Recommender Systems
    Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
    KDD 2023 | paper | codes
15. Mutually-regularized dual collaborative variational auto-encoder for recommendation systems
    Yaochen Zhu and Zhenzhong Chen
    WWW 2022 | paper | codes | slides
16. A multimodal variational encoder-decoder framework for micro-video popularity prediction
    Jiayi Xie*, Yaochen Zhu*, ..., Zhenzhong Chen (Short paper, * for equal contribution)
    WWW 2020 | paper | codes
17. Multimodal deep denoising framework for affective video content analysis
    Yaochen Zhu, Zhenzhong Chen, Feng Wu
    ACM MM 2019 | paper | Oral | image1 | image2 | image3 | image4

Journal Papers:

1. Global Graph Counterfactual Explanation: A Subgraph Mapping Approach
   Yinhan He, Wendy Zheng, Yaochen Zhu, Jing Ma, Saumitra Mishra, Natraj Raman, Ninghao Liu, Jundong Li
   TMLR, 2025. | paper | codes
2. Generative Risk Minimization for Out-of-Distribution Generalization on Graphs
   Song Wang, Zhen Tan, Yaochen Zhu, Chuxu Zhang, Jundong Li
   TMLR, 2025. | paper | codes
3. Disentangling User Interest and Geographical Context for POI Recommendations
   Wenhui Meng, Jiayi Xie, Jing Yi, Yaochen Zhu,Zhenzhong Chen
   ACM TIST, 2025. | paper | codes
4. Deep causal reasoning for recommendations
   Yaochen Zhu, Jing Yi, Jiayi Xie, Zhenzhong Chen
   ACM TIST, 2024. | paper | codes
5. Variational bandwidth auto-encoder for hybrid recommender systems
   Yaochen Zhu and Zhenzhong Chen
   IEEE TKDE, 2022. | paper | codes
6. Cross-modal variational auto-encoder for micro-video background music recommendation
   Jing Yi, Yaochen Zhu, Jiayi Xie, Zhenzhong Chen
   IEEE TMM, 2021. | paper | codes
7. Micro-video popularity prediction via multimodal variational information bottleneck
   Jiayi Xie, Yaochen Zhu, Zhenzhong Chen
   IEEE TMM, 2021. | paper | codes
8. Affective video content analysis via multimodal deep quality embedding network
   Yaochen Zhu, Zhenzhong Chen, Feng Wu
   IEEE TAFFC, 2020. | paper | codes

Tutorials:

1. Graph Prompting for Graph Learning Models: Recent Advances and Future Directions
   Xingbo Fu, Zehong Wang, Zihan Chen, Jiazhen Li, Yaochen Zhu, Zhenyu Lei, Cong Shen, Yanfang Ye, Chuxu Zhang, Jundong Li
   KDD 2025 (Accepted to the Survey Track) | paper | slides
2. Causal Inference with Latent Variables: Recent Advances and Future Perspectives
   Yaochen Zhu, Yinhan He, Jing Ma, Mengxuan Hu, Sheng Li, Jundong Li
   KDD 2024 (Accepted to the Survey Track) | paper | slides

Preprint:

1. Usable XAI: 10 strategies towards exploiting explainability in the LLM era
   Xuansheng Wu*, Haiyan Zhao*, Yaochen Zhu*, Yucheng Shi*, Fan Yang, Tianming Liu, Xiaoming Zhai, Jundong Li, Mengnan Du, Ninghao Liu
   ArXiv 2024 | paper | codes

Book Chapters:

1. Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and Generalization
   Yaochen Zhu, Jing Ma, Jundong Li
   Chapter 10, Machine Learning for Causal Inference, Springer | paper

Service

Awards