Zhicheng Dou

Professor, Renmin University of China

Zhicheng Dou
窦志成的中文主页
My Lab Members

Short Bio

Zhicheng Dou is currently a professor at Renmin University of China and vice dean for Gaoling School of Artificial Intelligence. He received his Ph.D. and B.S. degrees in computer science and technology from the Nankai University in 2008 and 2003, respectively. He worked at Microsoft Research Asia from July 2008 to September 2014. And since 2014, he has been a faculty member at Renmin University of China. His current research interests are Information Retrieval, Natural Language Processing , and Large Language Models. He is especially interested in Generative Information Retrieval. He received the Paper Award Nominations of TheWebConf 2023 (spotlight), the Best Paper Runner-Up Award from SIGIR 2013, and the Best Paper Award from AIRS 2012. He served as the program co-chair of SIGIR 2019 (short), AIRS 2017, and NTCIR-16/17. Zhicheng Dou is not a pure research guy - besides writing papers, he also enjoys writing codes to convert cool ideas into real systems.

Contacts:

I am recruiting highly-motivated students. Please drop an email to dou at ruc.edu.cn if you want to join my team.

Academic Homepages

Publications

Publication Filtering:

Just Accepted

  • [Accepted] Shuting Wang, Yutao Zhu, Zhicheng Dou. 2024. Embedding Prior Task-specific Knowledge into Language Models for Context-aware Document Ranking. In Proceedings of KDD 2025. (KDD 2025) (CCF A)
  • [Accepted] Hongjin Qian, Zheng Liu, Peitian Zhang, Kelong Mao, Yujia Zhou, Xu Chen, Zhicheng Dou. Tackling the Length Barrier: Dynamic Context Browsing for Knowledge-intensive Task. In Proceedings of KDD 2025. (KDD 2025) (CCF A)
  • [Accepted] Wang, Shuting; Dou, Zhicheng; Wang, Kexiang; Ma, Dehong; Fan, Jun; Shi, Daiting; Cheng, Zhicong; Gu, Simiu; Yin, Dawei; Wen, Ji-Rong. PRADA: Pre-train Ranking Models with Diverse Relevance Signals Mined from Search Logs. Accepted by TKDE.
  • [Accepted] Shuting Wang, Xin Yu, Mang Wang, Weipeng Chen, Yutao Zhu and Zhicheng Dou. 2025. RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generationg. In Proceedings of the 31st International Conference on Computational Linguistics, pages XXX-XXX, Abu Dhabi, UAE. International Committee on Computational Linguistics (COLING 2025)
  • [Accepted] Haobo Zhang, Qiannan Zhu, and Zhicheng Dou. 2025. Enhancing Reranking for Recommendation with LLMs through User Preference Retrieval. In Proceedings of the 31st International Conference on Computational Linguistics, pages xxxx–xxxx, Abu Dhabi, UAE. International Committee on Computational Linguistics (COLING 2025)
  • [Accepted] Huaying Yuan, Ziliang Zhao, Shuting Wang, Shitao Xiao, Minheng Ni, Zheng Liu and Zhicheng Dou. 2025. FineRAG: Fine-grained Retrieval-Augmented Text-to-Image Generation. In Proceedings of the 31st International Conference on Computational Linguistics, pages xxxx–xxxx, Abu Dhabi, UAE. International Committee on Computational Linguistics (COLING 2025)
2024

Preprints

  • Xiaoxi Li, Jiajie Jin, Yujia Zhou, Yuyao Zhang, Peitian Zhang, Yutao Zhu, Zhicheng Dou: From Matching to Generation: A Survey on Generative Information Retrieval. CoRR abs/2404.14851 (2024). (Arxiv)
  • Jiajie Jin, Yutao Zhu, Xinyu Yang, Chenghao Zhang, Zhicheng Dou: FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research. CoRR abs/2405.13576 (2024). (Arxiv Toolkit in Github)
  • Yutao Zhu, Zhaoheng Huang, Zhicheng Dou, Ji-Rong Wen: One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models. CoRR abs/2405.19670 (2024). (Arxiv)
  • Peitian Zhang, Ninglu Shao, Zheng Liu, Shitao Xiao, Hongjin Qian, Qiwei Ye, Zhicheng Dou: Extending Llama-3's Context Ten-Fold Overnight. CoRR abs/2404.19553 (2024). (Arxiv)
  • Peitian Zhang, Zheng Liu, Shitao Xiao, Ninglu Shao, Qiwei Ye, Zhicheng Dou: Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon. CoRR abs/2401.03462 (2024). (Arxiv)
  • Peitian Zhang, Zheng Liu, Shitao Xiao, Ninglu Shao, Qiwei Ye, Zhicheng Dou: Compressing Lengthy Context With UltraGist. CoRR abs/2405.16635 (2024). (Arxiv)
  • Shuting Wang, Xin Yu, Mang Wang, Weipeng Chen, Yutao Zhu, Zhicheng Dou: RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation. CoRR abs/2406.12566 (2024). (Arxiv)
  • Chenlong Deng, Kelong Mao, Zhicheng Dou: Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation. CoRR abs/2406.19760 (2024). (Arxiv)
  • Chenlong Deng, Kelong Mao, Yuyao Zhang, Zhicheng Dou: Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction. CoRR abs/2407.01964 (2024). (Arxiv)
  • Shuting Wang, Jiongnan Liu, Shiren Song, Jiehan Cheng, Yuqi Fu, Peidong Guo, Kun Fang, Yutao Zhu, Zhicheng Dou: DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation. CoRR abs/2406.05654 (2024). (Arxiv)
  • Guanting Dong, Yutao Zhu, Chenghao Zhang, Zechen Wang, Zhicheng Dou, Ji-Rong Wen: Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation. CoRR abs/2406.18676 (2024). (Arxiv)
  • Wenhan Liu, Yutao Zhu, Zhicheng Dou: DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Task. CoRR abs/2406.16332 (2024). (Arxiv)
  • Yutao Zhu, Kun Zhou, Kelong Mao, Wentong Chen, Yiding Sun, Zhipeng Chen, Qian Cao, Yihan Wu, Yushuo Chen, Feng Wang, Lei Zhang, Junyi Li, Xiaolei Wang, Lei Wang, Beichen Zhang, Zican Dong, Xiaoxue Cheng, Yuhan Chen, Xinyu Tang, Yupeng Hou, Qiangqiang Ren, Xincheng Pang, Shufang Xie, Wayne Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ze-Feng Gao, Yueguo Chen, Weizheng Lu, Ji-Rong Wen: YuLan: An Open-source Large Language Model. CoRR abs/2406.19853 (2024). (Arxiv)
  • Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Wayne Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen: Towards Effective and Efficient Continual Pre-training of Large Language Models. CoRR abs/2407.18743 (2024). (Arxiv)
  • Haonan Chen, Zhicheng Dou, Jiaxin Mao: Session-level Normalization and Click-through Data Enhancement for Session-based Evaluation. CoRR abs/2401.12445 (2024)。 (Arxiv)
  • Hongjin Qian, Zheng Liu, Peitian Zhang, Kelong Mao, Yujia Zhou, Xu Chen, Zhicheng Dou: Are Long-LLMs A Necessity For Long-Context Tasks? CoRR abs/2405.15318 (2024). (Arxiv)
  • Zhaoheng Huang, Zhicheng Dou, Yutao Zhu, Ji-Rong Wen: UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models. CoRR abs/2402.14690 (2024). (Arxiv)
  • Kelong Mao, Chenlong Deng, Haonan Chen, Fengran Mo, Zheng Liu, Tetsuya Sakai, Zhicheng Dou: ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval. CoRR abs/2404.13556 (2024). (Arxiv) Accepted by EMNLP 2024.

December

  • [Accepted] Yujia Zhou, Zheng Liu, and Zhicheng Dou. 2024. Boosting the Potential of Large Language Models with an Intelligent Information Assistant. In Proceedings of the Thirty-eighth Annual Conference on Neural Information Processing Systems. December 9-15, 2024. (NeurIPS 2024) (CCF A) ( Online | Download)

November

  • Kelong Mao, Chenlong Deng, Haonan Chen, Fengran Mo, Zheng Liu, Tetsuya Sakai, and Zhicheng Dou. 2024. ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1227–1240, Miami, Florida, USA. Association for Computational Linguistics. November 12–16, 2024. (EMNLP 2024) (CCF B) （Download | Online）
  • Chenlong Deng, Kelong Mao, and Zhicheng Dou. 2024. Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1253–1265, Miami, Florida, USA. Association for Computational Linguistics. November 12–16, 2024. (EMNLP 2024) (CCF B) （Download | Online）
  • Kelong Mao, Zheng Liu, Hongjin Qian, Fengran Mo, Chenlong Deng, Zhicheng Dou. 2024. RAG-Studio: Towards In-Domain Adaptation Of Retrieval Augmented Generation Through Self-Alignment. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 725–735, Miami, Florida, USA. Association for Computational Linguistics. November 12–16, 2024. (EMNLP 2024 Findings) （Download | Online）
  • Chenlong Deng, Kelong Mao, Yuyao Zhang, Zhicheng Dou. 2024. Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 2984–2993, Miami, Florida, USA. Association for Computational Linguistics. November 12–16, 2024. (EMNLP 2024 Findings) （ Download | Online）
  • Yutong Bai, Zhicheng Dou, Ji-Rong Wen. 2024. Learning Dynamic Multi-attribute Interest for Personalized Product Search. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 2984–2993, Miami, Florida, USA. Association for Computational Linguistics. November 12–16, 2024. (EMNLP 2024 Findings) （ Download | Download）

October

  • Ziliang Zhao, Zhicheng Dou and Yujia Zhou. 2024. Generating Intent-aware Clarifying Questions in Conversational Information Retrieval Systems. In Proceedings of the 33nd ACM International Conference on Information and Knowledge Management (CIKM ’24), October 21–25, 2024, Boise, Idaho, USA. ACM, New York, NY, USA, 10 pages. (CIKM 2024) (CCF B) ( Download | Online )

August

  • Haonan Chen, Zhicheng Dou*, Xuetong Hao, Yunhao Tao, Shiren Song, and Zhenli Sheng. 2024. Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-training. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’24), August 25–29, 2024, Barcelona, Spain. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3637528.3671513 (KDD 2024 ADS ) ( Download | DOI )
  • Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, and Zhicheng Dou*. 2024. INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2782–2809, Bangkok, Thailand. Association for Computational Linguistics. August 11-16 2024. (ACL 2024) (CCF A) (Download | DOI)
  • Jiejun Tan, Zhicheng Dou*, Yutao Zhu, Peidong Guo, Kun Fang, and Ji-Rong Wen. Small Models, Big Insights: Leveraging Slim Proxy Models to Decide When and What to Retrieve for LLMs. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4420–4436, Bangkok, Thailand. Association for Computational Linguistics. August 11-16 2024. (ACL 2024) (CCF A) (Download | DOI)
  • Hongjin Qian, Zheng Liu, Kelong Mao, Yujia Zhou, and Zhicheng Dou. Grounding Language Model with Chunking-Free In-Context Retrieval. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1298–1311, Bangkok, Thailand. Association for Computational Linguistics. August 11-16 2024. (ACL 2024) (CCF A) (Download | DOI)
  • Peitian Zhang, Shitao Xiao1, Zheng Liu, Zhicheng Dou, and Jian-Yun Nie. A Multi-Task Embedder For Retrieval Augmented LLMs. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3537–3553, Bangkok, Thailand. Association for Computational Linguistics. August 11-16 2024. (ACL 2024) (CCF A) (Download | DOI)
  • Haonan Chen, Zhicheng Dou*, Kelong Mao, Jiongnan Liu, and Ziliang Zhao. Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2700–2718, Bangkok, Thailand. Association for Computational Linguistics. August 11-16 2024. (ACL 2024) (CCF A) (Download | DOI)
  • Yiruo Cheng, Kelong Mao, and Zhicheng Dou*. Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2879–2893, Bangkok, Thailand. Association for Computational Linguistics. August 11-16 2024. (ACL 2024) (CCF A) (Download | DOI)
  • Jiajie Jin, Yutao Zhu, Yujia Zhou, and Zhicheng Dou. BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence. In Findings of the Association for Computational Linguistics ACL 2024, pages 750–761, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics. August 11-16 2024. (ACL Findings 2024) (Download | DOI)
  • Chenlong Deng, Zhicheng Dou, Yujia Zhou, Peitian Zhang, Kelong Mao. An Element is Worth a Thousand Words: Enhancing Legal Case Retrieval by Incorporating Legal Elements. In Findings of the Association for Computational Linguistics ACL 2024, pages 2354–2365, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics. August 11-16 2024. (ACL Findings 2024) (Download | DOI)

July

  • Xiaoxi Li, Zhicheng Dou, Yujia Zhou, and Fangchao Liu. 2024. CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '24). Association for Computing Machinery, New York, NY, USA, 26–37. https://dl.acm.org/doi/pdf/10.1145/3626772.3657778. July 14 - 18, 2024 (SIGIR 2024) (CCF A) (Download | DOI)
  • Peitian Zhang, Zheng Liu, Yujia Zhou, Zhicheng Dou, Fangchao Liu, and Zhao Cao. 2024. Generative Retrieval via Term Set Generation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '24). Association for Computing Machinery, New York, NY, USA, 458–468. https://dl.acm.org/doi/pdf/10.1145/3626772.3657797. July 14 - 18, 2024 (SIGIR 2024) (CCF A) (Download | DOI)
  • Zhirui Deng, Zhicheng Dou, Yutao Zhu, Xubo Qin, Pengchao Cheng, Jiangxu Wu, and Hao Wang. 2024. JDivPS: A Diversified Product Search Dataset. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '24). Association for Computing Machinery, New York, NY, USA, 1152–1161. https://dl.acm.org/doi/pdf/10.1145/3626772.3657888. July 14 - 18, 2024 (SIGIR 2024 Resource) (Download | DOI)

June

  • Yujia Zhou, Jing Yao, Zhicheng Dou, Yiteng Tu, Ledell Wu, Tat-Seng Chua, and Ji-Rong Wen. 2024. ROGER: Ranking-oriented Generative Retrieval. ACM Trans. Inf. Syst. Just Accepted (June 2024). https://dl.acm.org/doi/pdf/10.1145/3603167. Online AM: 03 June 2024 (TOIS 2024) (CCF A) (Download | DOI)

May

  • Wenhan Liu, Yujia Zhou, Yutao Zhu, Zhicheng Dou*. How to personalize and whether to personalize? Candidate documents decide. Knowledge and Information Systems Volumn 66, 5581–5604 (2024). Published: 27 May 2024 (KAIS 2024) (CCF B) (Download | DOI)
  • Zhan Su, Zhicheng Dou*, Yutao Zhu, and Ji-Rong Wen. 2024. Passage-aware Search Result Diversification. ACM Trans. Inf. Syst. 42, 5, Article 136 (September 2024), Pages 1 - 29. https://doi.org/10.1145/3653672. Published: 13 May 2024 (TOIS 2024) (CCF A) (Download | DOI)
  • Zheng Liu, Yujia Zhou, Yutao Zhu, Jianxun Lian, Chaozhuo Li, Zhicheng Dou, Defu Lian, and Jian-Yun Nie. 2024. Information Retrieval Meets Large Language Models. In Companion Proceedings of the ACM on Web Conference 2024 (WWW '24). Association for Computing Machinery, New York, NY, USA, 1586–1589. https://doi.org/10.1145/3589335.3641299. May 13–17, 2024, Singapore. (Download | DOI)
  • Wenhan Liu, Ziliang Zhao, Yutao Zhu, and Zhicheng Dou*. Mining Exploratory Queries for Conversational Search. In Proceedings of the ACM Web Conference 2024 (WWW '24). Association for Computing Machinery, New York, NY, USA, 1386–1394. https://doi.org/10.1145/3589334.3645424. May 13 - 17, 2024, Singapore. (TheWebConf 2024) (CCF A) (Download | DOI)
  • Ziliang Zhao and Zhicheng Dou*. Generating Multi-turn Clarification for Web Information Seeking. In Proceedings of the ACM Web Conference 2024 (WWW '24). Association for Computing Machinery, New York, NY, USA, 1539–1548. https://doi.org/10.1145/3589334.3645712. May 13 - 17, 2024, Singapore. (TheWebConf 2024) (CCF A) (Download | DOI)
  • Yujia Zhou Zheng Liu*, Jiajie Jin, Jian-Yun Nie, and Zhicheng Dou*. Metacognitive Retrieval-Augmented Large Language Models. In Proceedings of the ACM Web Conference 2024 (WWW '24). Association for Computing Machinery, New York, NY, USA, 1453–1463. https://doi.org/10.1145/3589334.3645481. May 13 - 17, 2024, Singapore. (TheWebConf 2024) (CCF A) (Download | DOI)
  • Yujia Zhou, Qiannan Zhu, Jiajie Jin, and Zhicheng Dou*. Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism. In Proceedings of the ACM Web Conference 2024 (WWW '24). Association for Computing Machinery, New York, NY, USA, 1464–1473. https://doi.org/10.1145/3589334.3645482. May 13 - 17, 2024, Singapore. (TheWebConf 2024) (CCF A) (Download | DOI)

April

  • Qi Liu, Gang Guo, Jiaxin Mao, Zhicheng Dou, Ji-Rong Wen, Hao Jiang, Xinyu Zhang, and Zhao Cao. 2024. An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models. ACM Trans. Inf. Syst. 42, 5, Article 118 (September 2024), pages 1- 28. https://doi.org/10.1145/3639818. Published: 29 April 2024 (TOIS 2024) (CCF A) (Download | DOI)
  • Zhirui Deng, Zhicheng Dou*, Zhan Su, and Ji-Rong Wen. 2024. Multi-grained Document Modeling for Search Result Diversification. ACM Trans. Inf. Syst. Volumn 42, Issue 5, Article 126 (September 2024), pages 1-22. Published: 27 April 2024 (TOIS 2024) (CCF A) (Download | DOI)

March

  • Zhirui Deng, Zhicheng Dou*, Yutao Zhu, and Ji-Rong Wen. 2024 CL4DIV: A Contrastive Learning Framework for Search Result Diversification. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM '24). Association for Computing Machinery, New York, NY, USA, 171–180. https://doi.org/10.1145/3616855.3635851. March 4 - 8, 2024 (WSDM 2024) (CCF B) (Download | DOI)
  • Zhu, Qiannan, Haobo Zhang, Qing He and Zhicheng Dou*. Query-Aware Explainable Product Search With Reinforcement Knowledge Graph Reasoning. IEEE Transactions on Knowledge and Data Engineering 36 (2024): 1260-1273. March 2024. Date of Publication: 24 July 2023 (TKDE 2024) (CCF A) (Download | DOI)

February

  • Xiaoxi Li, Yujia Zhou, Zhicheng Dou*. UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models. Proceedings of the 38th AAAI Conference on Artificial Intelligence, 8688-8696. February 20–27, 2024, Vancouver, Canada. (AAAI 2024) (CCF A) (Download | DOI)
  • Yutong Bai, Yujia Zhou, Zhicheng Dou*, and Ji-Rong Wen. 2024. Intent-Oriented Dynamic Interest Modeling for Personalized Web Search. ACM Trans. Inf. Syst. 42, 4, Article 96 (July 2024), pages 1-30. Published: 09 February 2024 https://doi.org/10.1145/3639817 (TOIS 2024) (CCF A) (Download | DOI)
  • Jiongnan Liu, Zhicheng Dou*, Jian-Yun Nie, Ji-Rong Wen: Integrated Personalized and Diversified Search Based on Search Logs. IEEE Trans. Knowl. Data Eng. 36(2): 694-707 (2024). February 2024. Date of Publication: 30 June 2023. (TKDE 2024) (CCF A) (Download | DOI)

January

  • Shuting Wang, Zhicheng Dou*, Jiongnan Liu, Qiannan Zhu, and Ji-Rong Wen. 2024. Personalized and Diversified: Ranking Search Results in an Integrated Way. ACM Trans. Inf. Syst. 42, 3, Article 81 (May 2024), Pages 1 - 25. https://doi.org/10.1145/3631989. Published: 22 January 2024 (TOIS 2024) (CCF A) (Download | DOI)
2023

Preprints

  • Hongjing Qian, Yutao Zhu, Zhicheng Dou, Haoqi Gu, Xinyu Zhang, Zheng Liu, Ruofei Lai, Zhao Cao, Jian-Yun Nie, Ji-Rong Wen: WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus. https://arxiv.org/abs/2304.04358 (2023) (DOI)
  • Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zhicheng Dou, Ji-Rong Wen: Large Language Models for Information Retrieval: A Survey. CoRR abs/2308.07107 (aXiv | Download ) (2023)
  • Peitian Zhang, Shitao Xiao, Zheng Liu, Zhicheng Dou, Jian-Yun Nie: Retrieve Anything To Augment Large Language Models. CoRR abs/2310.07554 (2023) (aXiv | Download )
  • Hongjin Qian, Zhicheng Dou, Jiejun Tan, Haonan Chen, Haoqi Gu, Ruofei Lai, Xinyu Zhang, Zhao Cao, Ji-Rong Wen: Optimizing Factual Accuracy in Text Generation through Dynamic Knowledge Selection. CoRR abs/2308.15711 (2023) (aXiv | Download )
  • Jiongnan Liu, Jiajie Jin, Zihan Wang, Jiehan Cheng, Zhicheng Dou, Ji-Rong Wen: RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit. https://arxiv.org/abs/2306.05212 (2023) (DOI)

December 2023

  • Yujia Zhou, Zhicheng Dou*, and Ji-Rong Wen. 2023. Enhancing Generative Retrieval with Reinforcement Learning from Relevance Feedback. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12481–12490, Singapore. Association for Computational Linguistics. December 6 –10, 2023. (EMNLP 2023) (CCF B) （ Online | Download）
  • Peitian Zhang, Zheng Liu, Shitao Xiao, Zhicheng Dou, and Jing Yao. 2023. Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1877–1888, Singapore. Association for Computational Linguistics. December 6 –10, 2023. (EMNLP 2023) (CCF B) （ Online | Download）
  • Kelong Mao, Zhicheng Dou*, Fengran Mo, Jiewen Hou, Haonan Chen, and Hongjin Qian. 2023. Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1211–1225, Singapore. Association for Computational Linguistics. December 6 –10, 2023. (EMNLP 2023 Findings) （ Online | Download）

November 2023

  • Yujia Zhou, Jing Yao, Ledell Wu, Zhicheng Dou* and Ji-Rong Wen. 2023. WebUltron: An Ultimate Retriever on Webpages Under the Model-Centric Paradigm. IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 9, pp. 4996-5006, doi: 10.1109/TKDE.2023.3332858. September 2024. Date of Publication: 15 November 2023 (TKDE 2023) (CCF A) ( Online | Download ）

October 2023

  • Zhirui Deng, Zhicheng Dou*, Ji-Rong Wen: DeepQFM: a deep learning based query facets mining method. Information Retrieval Journal. Volume 26, article number 9, (2023). Published: 30 October 2023. (Online | Download )
  • Zihan Wang, Yujia Zhou, Yiteng Tu, and Zhicheng Dou*. 2023. NOVO: Learnable and Interpretable Document Identifiers for Model-Based IR. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM ’23), October 21–25, 2023, Birmingham, United Kingdom. ACM, New York, NY, USA, 10 pages. (CIKM 2023) (CCF B) ( Download | DOI )
  • Huaying Yuan, Zhicheng Dou*, Yujia Zhou, Yu Guo, and Ji-Rong Wen. 2023. VILE: Block-Aware Visual Enhanced Document Retrieval. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM ’23), October 21–25, 2023, Birmingham, United Kingdom. ACM, New York, NY, USA, 10 pages. (CIKM 2023) (CCF B) ( Download | DOI )

August 2023

  • Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren, Pengjie Ren, Zhiqiang Wang, Mingwen Wang, Ji-Rong Wen, Le Wu, Xin Xin, Jun Xu, Dawei Yin, Peng Zhang, Fan Zhang, Weinan Zhang, Min Zhang, Xiaofei Zhu: Information Retrieval meets Large Language Models: A strategic report from Chinese IR community. AI Open 4: 80-90 (2023). Available online 7 August 2023 (DOI | Download )
  • Zhan Su, Zhicheng Dou*, Yujia Zhou, Ziyuan Zhao, and Ji-Rong Wen. 2023. PSLOG: Pretraining with Search Logs for Document Ranking. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’23), August 6–10, 2023, Long Beach, CA, USA. ACM, New York, NY, USA, 11 pages. (KDD 2023) (CCF A) ( Download | DOI )
  • Shitong Dai, Jiongnan Liu, Zhicheng Dou*, Haonan Wang, Lin Liu, Bo Long, and Ji-Rong Wen. 2023. Contrastive Learning for User Sequence Representation in Personalized Product Search. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’23), August 6–10, 2023, Long Beach, CA, USA. ACM, New York, NY, USA, 11 pages. (KDD 2023) (CCF A) ( Download | DOI )
  • Ziliang Zhao, Zhicheng Dou*, Yu Guo, Zhao Cao, and Xiaohua Cheng. 2023. Improving Search Clarification with Structured Information Extracted from Search Results. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’23), August 6–10, 2023, Long Beach, CA, USA. ACM, New York, NY, USA, 10 pages. (KDD 2023) (CCF A) ( Download | DOI )
  • Zihan Wang, Hongjin Qian, Zhicheng Dou. Learning on Structured Documents for Conditional Question Answering. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 583–599, Harbin, China. Chinese Information Processing Society of China. August 2 – 5, 2023. (CCL 2023) ( Download | DOI )
  • Han Zhang, Zhicheng Dou. Case Retrieval for Legal Judgment Prediction in Legal Artificial Intelligence. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 801–812, Harbin, China. Chinese Information Processing Society of China. August 2 – 5, 2023. (CCL 2023) ( Download | DOI )

July 2023

  • Jiongnan Liu, Zhicheng Dou*, Guoyu Tang, and Sulong Xu. 2023. JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23), July 23–27, 2023, Taipei, Taiwan. ACM, New York, NY, USA, 8 pages. (SIGIR 2023) ( Download | DOI )
  • Search-oriented Conversational Query Editing. Kelong Mao, Zhicheng Dou*, Bang Liu, Hongjin Qian, Fengran Mo, Xiangli Wu, Xiaohua Cheng and Zhao Cao. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4160–4172, Toronto, Canada. Association for Computational Linguistics. July 10-12, 2023. (ACL 2023 - Findings) ( Download | DOI )
  • Hence, Socrates is mortal: A Benchmark for Natural Language Syllogistic Reasoning. Yongkang Wu, Meng Han, Yutao Zhu, Lei Li, Xinyu Zhang, Ruofei Lai, Xiaoguang Li, Yuanhang Ren, Zhicheng Dou, and Zhao Cao. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4160–4172, Toronto, Canada. Association for Computational Linguistics. July 10-12, 2023. (ACL 2023 - Findings) ( Download | DOI )

May 2023

  • Kelong Mao, Hongjin Qian, Fengran Mo, Zhicheng Dou*, Bang Liu, Xiaohua Cheng, and Zhao Cao. 2023. Learning Denoised and Interpretable Session Representation for Conversational Search. In Proceedings of the ACM Web Conference 2023 (WWW ’23) (Spotlight paper!), May 1–5, 2023, Austin, TX, USA. ACM, New York, NY, USA, 11 pages. (WWW 2023) (CCF A) ( Download | DOI )
  • Shuting Wang, Zhicheng Dou, Jing Yao, Yujia Zhou, and Ji-Rong Wen. 2023. Incorporating Explicit Subtopics in Personalized Search. In Proceedings of the ACM Web Conference 2023 (WWW ’23), May 1–5, 2023, Austin, TX, USA. ACM, New York, NY, USA, 11 pages. (WWW 2023) (CCF A) ( Download | DOI )

May 2023

  • Han Zhang, Zhicheng Dou*, Yutao Zhu and Ji-rong Wen. Contrastive Learning for Legal Judgment Prediction. ACM Transactions on Information Systems. 41(4): 113:1-113:25 (2023). Published: 21 April 2023 (TOIS) (CCF A) ( Download | DOI )

April 2023

  • Xubo Qin, Zhicheng Dou, Yutao Zhu, and Ji-Rong Wen. 2022. GDESA: Greedy Diversity Encoder with Self-Attention for Search Results Diversification. ACM Trans. Inf. Syst. 41(2): 34:1-34:36 (2023) . Published: 03 April 2023. (TOIS 2023) (CCF A) (Download | DOI)
  • Hongjin Qian and Zhicheng Dou*. 2023. Topic-Enhanced Personalized Retrieval-Based Chatbot. In Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II. Springer-Verlag, Berlin, Heidelberg, 79–93. (ECIR 2023) ( Download | DOI )
  • Yujia Zhou, Zhicheng Dou* and Ji-Rong Wen, Enhancing Potential Re-finding in Personalized Search with Hierarchical Memory Networks, in IEEE Transactions on Knowledge and Data Engineering. 35(4): 3846-3857 (2023). 01 April 2023. Date of Publication: 09 November 2021 (TKDE 2023) (CCF A) (Download | DOI)

February 2023

  • Qingyu Bing, Qiannan Zhu, and Zhicheng Dou*. 2023. Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM ’23), February 27-March 3, 2023, Singapore, Singapore. ACM, New York, NY, USA, 9 pages. (WSDM 2023) (CCF B) ( Download | DOI )
  • Shuting Wang, Zhicheng Dou*, and Yutao Zhu. 2023. Heterogeneous Graph-based Context-aware Document Ranking. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM ’23), February 27-March 3, 2023, Singapore, Singapore. ACM, New York, NY, USA, 9 pages. (WSDM 2023) (CCF B) ( Download | DOI )
  • Jing Yao, Zheng Liu, Junhan Yang, Zhicheng Dou, Xing Xie and Ji-rong Wen. CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection. ACM Transactions on Intelligent Systems and Technology, 14(2): 32:1-32:24 (2023). Published: 16 February 2023. (Download | DOI)
  • Yuhang Ye, Zhonghua Li, Zhicheng Dou, Yutao Zhu, Changwang Zhang, Shangquan Wu, Zhao Cao. Learning from the Wisdom of Crowds: Exploiting Similar Sessions for Session Search. In Proceedings of the AAAI Conference on Artificial Intelligence: 4818-4826. February 7 – 14, 2023. (AAAI 2023) (CCF A) ( Download | DOI )

January 2023

  • Yujia Zhou, Jing Yao, Zhicheng Dou*, Ledell Yu Wu and Ji-rong Wen. DynamicRetriever: A Pre-trained Model-based IR System Without an Explicit Index. Machine Intelligence Research. 20(2): 276-288 (2023). Published: 11 January 2023. ( Download | DOI )
  • Haonan Chen, Zhicheng Dou*, Qiannan Zhu, Xiaochen Zuo, and Ji-Rong Wen. Integrating Representation and Interaction for Context-aware Document Ranking. ACM Trans. Inf. Syst. 41(1): 21:1-21:23 (2023). Published: 10 January 2023. (TOIS 2023) (CCF A) (Download | DOI)
2022
  • Kelong Mao, Zhicheng Dou*, Hongjin Qian, Fengran Mo, Xiaohua Cheng, and Zhao Cao. 2022. ConvTrans: Transforming Web Search Sessions for Conversational Dense Retrieval. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2935–2946, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics (EMNLP 2022) (CCF B) (Download | URL)
  • Hongjin Qian and Zhicheng Dou*. 2022. Explicit Query Rewriting for Conversational Dense Retrieval. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4725–4737, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics (EMNLP 2022) (CCF B) (Download | URL)
  • Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang, Yutao Zhu, Zheng Liu, Jiawen Wu, Dejiang Kong, Ruofei Lai, Zhao Cao, Zhicheng Dou, and Xipeng Qiu. 2022. Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4952–4964, Gyeongju, Republic of Korea. International Committee on Computational Linguistics (COLING 2022) (CCF B) (Download | URL)
  • Zhaoheng Huang, Zhicheng Dou*, Yutao Zhu, and Zhengyi Ma. 2022. MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1030–1042, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. (EMNLP 2022 Findings) (CCF B) (Download | URL)
  • Zhan Su, Zhicheng Dou*, Yutao Zhu, and Ji-Rong Wen. 2022. Knowledge Enhanced Search Result Diversification. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22). Association for Computing Machinery, New York, NY, USA, 1687–1695. (KDD 2022) (CCF A) ( Download | DOI)
  • Yutao Zhu, Jian-Yun Nie, Yixuan Su, Haonan Chen, Xinyu Zhang, and Zhicheng Dou*. 2022. From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM ’22), October 17–21, 2022, Atlanta, GA, USA. ACM, New York, NY, USA, 11 pages. (CIKM 2022) (CCF B) ( Download | DOI)
  • Haonan Chen, Zhicheng Dou*, Yutao Zhu, Zhao Cao, Xiaohua Cheng, and Ji-Rong Wen. 2022. Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM ’22), October 17–21, 2022, Atlanta, GA, USA. ACM, New York, NY, USA, 11 pages. (CIKM 2022) (CCF B) ( Download | DOI)
  • Hanxun Zhong, Zhicheng Dou*, Yutao Zhu, Hongjin Qian, and Ji-Rong Wen. 2022. Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5808–5820, Seattle, United States. Association for Computational Linguistics. (NAACL 2022) (CCF B) ( Download | DOI )
  • Kelong Mao, Zhicheng Dou*, and Hongjin Qian. Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval. In Proceedings of SIGIR 2022. (SIGIR 2022) (CCF A) (Download | DOI)
  • Yu Guo, Zhengyi Ma, Jiaxin Mao, Hongjin Qian, Xinyu Zhang, Hao Jiang, Zhao Cao, and Zhicheng Dou*. Webformer: Pre-training with Web Pages for Information Retrieval. In Proceedings of SIGIR 2022. (SIGIR 2022) (CCF A) (Download | DOI)
  • Ziliang Zhao, Zhicheng Dou*, Jiaxin Mao and Ji-Rong Wen. Generating Clarifying Questions with Web Search Results. In Proceedings of SIGIR 2022. (SIGIR 2022) (CCF A) (Download | DOI)
  • Yujia Zhou, Zhicheng Dou*, Huaying Yuan and Zhengyi Ma. Socialformer: Social Network Inspired Long Document Modeling for Document Ranking. In Proceedings of the ACM Web Conference 2022 (WWW ’22), April 25–29, 2022, Virtual Event, Lyon, France. ACM, New York, NY, USA. (TheWebConf 2022) (CCF A) (Download | DOI)
  • Jiongnan Liu, Zhicheng Dou*, Qiannan Zhu, and Ji-Rong Wen. A Category-aware Multi-interest Model for Personalized Product Search. In Proceedings of the ACM Web Conference 2022 (WWW ’22), April 25–29, 2022, Virtual Event, Lyon, France. ACM, New York, NY, USA. (TheWebConf 2022) (CCF A) (Download | DOI)
  • Qiannan Zhu, Haobo Zhang, Qing He, and Zhicheng Dou*. A Gain-Tuning Dynamic Negative Sampler for Recommendation. In Proceedings of the ACM Web Conference 2022 (WWW ’22), April 25–29, 2022, Virtual Event, Lyon, France. ACM, New York, NY, USA. (TheWebConf 2022) (CCF A) (Download | DOI)
  • Xiaochen Zuo, Zhicheng Dou*, Ji-Rong Wen. Improving Session Search by Modeling Multi-Granularity Historical Query Change. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM '22). ACM, New York, NY, USA. (WSDM 2022) (CCF B) (Download | DOI)
  • Chenlong Deng, Yujia Zhou, and Zhicheng Dou*. Improving Personalized Search with Dual-Feedback Network. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM '22). ACM, New York, NY, USA, (WSDM 2022) (CCF B) (Download | DOI)
  • Chengzhen Fu, Enrui Hu, Letian Feng, Zhicheng Dou, Yantao Jia, Lei Chen, Pan Yu, and Zhao Cao. Leveraging Multi-view Inter-passage Interactions for Neural Document Ranking. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM '22). ACM, New York, NY, USA, (WSDM 2022) (CCF B) (Download | DOI)
2021
  • [J] Yutao Zhu, Ruihua Song, Jian-Yun Nie, Pan Du, Zhicheng Dou, and Jin Zhou. 2021. Leveraging Narrative to Generate Movie Script. ACM Trans. Inf. Syst. Just Accepted (December 2021). (TOIS 2021) (CCF A) (Download | DOI)
  • [J] Jing Yao, Zhicheng Dou*, and Ji-Rong Wen. 2021. Clarifying Ambiguous Keywords with Personal Word Embeddings for Personalized Search. ACM Trans. Inf. Syst. 40, 3, Article 43, 29 pages. (TOIS 2021) (CCF A) (Download | DOI)
  • [J] Jing Yao, Zhicheng Dou*, Jian-Yun Nie, and Ji-Rong Wen. Looking Back on the Past: Active Learning with Historical Evaluation Results. in IEEE Transactions on Knowledge and Data Engineering. (TKDE 2021) (CCF A) (Download | DOI)
  • [J] Jing Yao, Zhicheng Dou*, Jun Xu, and Ji-Rong Wen. RLPS: A Reinforcement Learning based Framework for Personalized Search. in Transactions on Information Systems (TOIS 2021) (CCF A) (Download | DOI)
  • Yujia Zhou, Zhicheng Dou*, Yutao Zhu, Ji-Rong Wen. 2021. PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM ’21), November 1–5, 2021, Virtual Event, QLD, Australia. ACM, New York, NY, USA, 10 pages. (CIKM 2021) (CCF B) (Download | DOI)
  • Hongjin Qian, Zhicheng Dou*, Yutao Zhu, Yueyuan Ma, and Ji-Rong Wen. 2021. Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM ’21), November 1–5, 2021, Virtual Event, QLD, Australia. ACM, New York, NY, USA, 11 pages. (CIKM 2021) (CCF B) (Download | DOI)
  • Jing Yao, Zhicheng Dou*, Ruobing Xie, Yanxiong Lu, Zhiping Wang and Ji-Rong Wen. 2021. USER: A Unified Information Search and Recom-mendation Model based on Integrated Behavior Sequence. InProceedings ofthe 30th ACM International Conference on Information and Knowledge Man-agement (CIKM ’21), November 1–5, 2021, Virtual Event, QLD, Australia.ACM,New York, NY, USA, 11 pages. (CIKM 2021) (CCF B) (Download | DOI)
  • Zhengyi Ma, Zhicheng Dou*, Wei Xu, Xinyu Zhang, Hao Jiang, Zhao Cao,and Ji-Rong Wen. 2021. Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need. InProceedings of the 30th ACM International Conference onInformation and Knowledge Management (CIKM ’21), November 1–5, 2021,Virtual Event, QLD, Australia.ACM, New York, NY, USA, 10 pages (CIKM 2021) (CCF B) (Download | DOI)
  • Yutao Zhu, Jian-Yun Nie, Zhicheng Dou*, Zhengyi Ma, Xinyu Zhang, Pan Du, Xiaochen Zuo, and Hao Jiang. 2021. Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking . InProceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM ’21), New York, NY, USA, 2780–2791. (CIKM 2021) (CCF B) (Download | DOI)
  • Shuqi Lu, Chenyan Xiong, Di He, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk. Less is More: Pre-training a Strong Siamese Encoder Using a Weak Decoder. The 2021 Conference on Empirical Methods in Natural Language Processing. (EMNLP 2021) (CCF B)
  • Zhengyi Ma, Zhicheng Dou*, Yutao Zhu, Hanxun Zhong, and Ji-Rong Wen. One Chatbot Per Person: Creating Personalized Chatbots based onImplicit User Profiles . InProceedings of the 44th International ACM SIGIRConference on Research and Development in Information Retrieval (SIGIR ’21),July 11–15, 2021, Virtual Event, Canada.ACM, New York, NY, USA, 10 pages. (SIGIR 2021) (CCF A) (Download | DOI)
  • Zhan Su, Zhicheng Dou*,Yutao Zhu, Xubo Qin, and Ji-Rong Wen. Modeling Intent Graph for Search Result Diversification. InProceedings of the 44th International ACM SIGIRConference on Research and Development in Information Retrieval (SIGIR ’21),July 11–15, 2021, Virtual Event, Canada.ACM, New York, NY, USA, 10 pages. (SIGIR 2021) (CCF A) (Download | DOI)
  • Yujia Zhou, Zhicheng Dou*, Bingzheng Wei, Ruobing Xie, and Ji-Rong Wen. Group based Personalized Search by Integrating Search Behaviourand Friend Network. InProceedings of the 44th International ACM SIGIRConference on Research and Development in Information Retrieval (SIGIR ’21),July 11–15, 2021, Virtual Event, Canada.ACM, New York, NY, USA, 10 pages. (SIGIR 2021) (CCF A) (Download | DOI)
  • Hongjin Qian, Xiaohe Li, Hanxun Zhong, Yu Guo, Yueyuan Ma, Yutao Zhu, Zhanliang Liu, Zhicheng Dou*, and Ji-Rong Wen. Pchatbot: A Large-Scale Dataset for Personalized Chatbot. InProceedings of the 44th International ACM SIGIRConference on Research and Development in Information Retrieval (SIGIR ’21),July 11–15, 2021, Virtual Event, Canada.ACM, New York, NY, USA, 8 pages (Resource Paper). (SIGIR 2021) (CCF A) (Download | DOI)
  • Xinyu Zhang, Ke Zhan, Enrui Hu, Chengzhen Fu, Lan Luo, Hao Jiang, Yantao Jia, Pan Yu, Zhao Cao, Zhicheng Dou, Lei Chen. Answer Complex Questions: Path Ranker Is All You Need. InProceedings of the 44th International ACM SIGIRConference on Research and Development in Information Retrieval (SIGIR ’21),July 11–15, 2021, Virtual Event, Canada.ACM, New York, NY, USA, 10 pages. (SIGIR 2021) (CCF A) (Download | DOI)
  • Yutao Zhu, Jian-Yun Nie, Kun Zhou, Pan Du, Hao Jiang, andZhicheng Dou. Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals. InProceedings of the 44th International ACM SIGIRConference on Research and Development in Information Retrieval (SIGIR ’21),July 11–15, 2021, Virtual Event, Canada.ACM, New York, NY, USA, 5 pages (Short Paper). (SIGIR 2021) (CCF A) (Download | DOI)
  • Jing Yao, Zhicheng Dou*, and Ji-Rong Wen. FedPS: A Privacy Protection Enhanced Personalized Search Framework. Proceedings of 30th The Web Conference (WWW 2021) (CCF A) (Download)
  • Yutao Zhu, Kun Zhou, Jian-Yun Nie, Shengchao Liu, and Zhicheng Dou. Neural Sentence Ordering Based on Constraint Graphs. Proceedings of The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021) (CCF A) (Download)
  • Yutao Zhu, Jian-Yun Nie, Kun Zhou, Pan Du, and Zhicheng Dou. Content Selection Network for Document-grounded Retrieval-based Chatbots. Proceedings of the 43rd edition of the annual BCS-IRSG European Conference on Information Retrieval (ECIR 2021) (Download)
  • Zhumin Chen, Xueqi Cheng, Shoubin Dong, Zhicheng Dou, Jiafeng Guo, Xuanjing Huang, Yanyan Lan, Chenliang Li, Ru Li, Tie-Yan Liu, Yiqun Liu, Jun Ma, Bing Qin, Mingwen Wang, Ji-Rong Wen, Jun Xu, Min Zhang, Peng Zhang, Qi Zhang: Information retrieval: a view from the Chinese IR community. Frontiers Comput. Sci. 15(1): 151601 (2021) (Download | DOI)
  • Yuqi Huo, Manli Zhang, Guangzhen Liu, Haoyu Lu, Yizhao Gao, Guoxing Yang, Jingyuan Wen, Heng Zhang, Baogui Xu, Weihao Zheng, Zongzheng Xi, Yueqian Yang, Anwen Hu, Jinming Zhao, Ruichen Li, Yida Zhao, Liang Zhang, Yuqing Song, Xin Hong, Wanqing Cui, Dan Yang Hou, Yingyan Li, Junyi Li, Peiyu Liu, Zheng Gong, Chuhao Jin, Yuchong Sun, Shizhe Chen, Zhiwu Lu, Zhicheng Dou, Qin Jin, Yanyan Lan, Wayne Xin Zhao, Ruihua Song, Ji-Rong Wen: WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training. https://arxiv.org/pdf/2103.06561.pdf (2021)
  • Xubo Qin, Zhicheng Dou, Yutao Zhu, Ji-Rong Wen: Interaction-Based Document Matching for Implicit Search Result Diversification. CCIR 2021: 3-15
  • Han Zhang, Zhicheng Dou, Yutao Zhu, Jirong Wen: Few-Shot Charge Prediction with Multi-grained Features and Mutual Information. CCL 2021: 387-403
  • Xubo Qin, Zhicheng Dou, Yutao Zhu, and Jirong Wen. 2021. 基于双星型自注意力网络的搜索结果多样化方法(Search Result Diversification Framework Based on Dual Star-shaped Self-Attention Network). In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 280–292, Huhhot, China. Chinese Information Processing Society of China. (URL)
  • 郭宇,窦志成,文继荣.PCC:一个对单用户建模的个性化对话系统[J].中文信息学报,2021,35(12):112-121.
  • 张晗,郑伟昊,窦志成,文继荣.融合法律文本结构信息的刑事案件判决预测[J/OL].计算机工程与应用:1-12[2023-01-24]
2020
  • Zhengyi Ma, Zhicheng Dou*, Guanyue Bian, and Ji-Rong Wen. PSTIE: Time Information Enhanced Personalized Search. Proceedings of 29th ACM International Conference on Information and Knowledge Management (CIKM 2020) (CCF B) (Download | DOI)
  • Xubo Qin, Zhicheng Dou* and Ji-Rong Wen. Diversifying Search Results using Self-Attention Network. Proceedings of 29th ACM International Conference on Information and Knowledge Management (CIKM 2020) (CCF B) (Download | DOI)
  • Jing Yao, Zhicheng Dou* and Ji-Rong Wen. Employing Personal Word Embeddings for Personalized Search. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020) (CCF A) (Download | DOI)
  • Yujia Zhou, Zhicheng Dou* and Ji-Rong Wen. Encoding History with Context-aware Representation Learning for Personalized Search. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020) (CCF A) (Download | DOI)
  • Jiongnan Liu, Zhicheng Dou*, Xiaojie Wang, Shuqi Lu and Ji-Rong Wen. DVGAN: A Minimax Game for Search Result Diversification Combining Explicit and Implicit Features. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020) (CCF A) (Download | DOI)
  • Shuqi Lu, Zhicheng Dou*, Chenyan Xiong, Xiaojie Wang and Ji-Rong Wen. Knowledge Enhanced Personalized Search. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020) (CCF A) (Download | DOI)
  • Jing Yao, Zhicheng Dou*, Jun Xu, and Ji-Rong Wen. RLPer: A Reinforcement Learning Model for Personalized Search. Proceedings of The Web Conference 2020, April 20--24, 2020, Taipei, Taiwan (WWW 2020) (CCF A) (Download | DOI)
  • Anwen Hu, Zhicheng Dou*, Jian-Yun Nie, and Ji-Rong Wen. Leveraging Multi-token Entities in Document-level Named Entity Recognition. Proceedings of the thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020) (CCF A) (Download | DOI)
  • Yujia Zhou, Zhicheng Dou*, and Ji-Rong Wen. 2020. Enhancing Re-finding Behavior with External Memories for Personalized Search. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining (WSDM '20). ACM, New York, NY, USA, (WSDM 2020) (CCF B) (Download | DOI)
  • Yutao Zhu, Ruihua Song, Zhicheng Dou*, Jian-Yun Nie, and Jin Zhou. ScriptWriter: Narrative-Guided Script Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, {ACL} 2020, Online, July 5-10, 2020: 8647-8657 (ACL 2020) (CCF A) (Download | DOI)
2019
  • Shuqi Lu, Zhicheng Dou*, Xu Jun, Jian-Yun Nie, and Ji-Rong Wen. PSGAN: A Minimax Game for Personalized Search with Limited and Noisy Click Data. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval: 555-564 (SIGIR 2019) (CCF A) (Download | DOI)
  • Zhicheng Dou*, Xue Yang, Diya Li, Ji-Rong Wen, and Tetsuya Sakai. Low-cost, bottom-up measures for evaluating search result diversification. Information Retrieval Journal (2019). (IRJ) (Download | DOI)
  • Yutao Zhu, Zhicheng Dou*, Jian-Yun Nie, and Ji-Rong Wen. ReBoost: A Retrieval-Boosted Sequence-to-Sequence Model for Neural Response Generation. Information Retrieval Journal (2019). (IRJ) (Download | DOI)
  • Juan Li, Zhicheng Dou*, Yutao Zhu, Xiaochen Zuo, and Ji-Rong Wen. Deep Cross-platform Product Matching in E-commerce. Information Retrieval Journal (2019). (IRJ) (Download | DOI)
  • Yujia Zhou, , Zhicheng Dou*, Songwei Ge, and Ji-Rong Wen. Dynamic Personalized Search Based on RNN with Attention Mechanism. Chinese Journal of Computer (2019), Vol 42. (Download | DOI)
  • Zhicheng Dou*, Xubo Qin, and Ji-Rong Wen. A Survey on Search Result Diversification. Chinese Journal of Computer (2019), Vol 42. (Download | DOI)
  • Shuqi Lu, Zhicheng Dou*, and Ji-Rong Wen. Research On Structural Data Extraction in Surgical Cases. Chinese Journal of Computer (2019), Vol 42. (Download | DOI)
  • Xiaochen Zuo, Zhicheng Dou*, Zhen Huang, Shuqi Lu. Product Category Mining Associated with Weibo Hot Topics. Journal of Computer Research and Development,2019, 56(09):1927-1938. (Download | DOI)
  • Anwen Hu, Zhicheng Dou*, and Ji-Rong Wen. Document-Level Named Entity Recognition by Incorporating Global and Neighbor Features. Information Retrieval. 25th China Conference, CCIR 2019, Fuzhou, China, September 20–22, 2019, Proceedings. (Download | DOI)
2018
  • Zhengbao Jiang, Zhicheng Dou*, Wayne Xin Zhao, Jian-Yun Nie, Ming Yue, and Ji-Rong Wen. Supervised Search Result Diversification via Subtopic Attention. IEEE Trans. Knowl. Data Eng. 30(10): 1971-1984 (2018) (TKDE) (CCF A) (Download | DOI)
  • Xiao-Jie Wang, Ji-Rong Wen, Zhicheng Dou*, Tetsuya Sakai, and Rui Zhang. Search Result Diversity Evaluation Based on Intent Hierarchies, IEEE Trans. Knowl. Data Eng. 30(1): 156-169 (2018) (TKDE) (CCF A) (Download | DOI)
  • Songwei Ge, Zhicheng Dou*, Zhengbao Jiang, Jian-Yun Nie, Ji-Rong Wen. Personalizing Search Results Using Hierarchical RNN with Query-aware Attention. in Proceedings of the 27th ACM International Conference on Information and Knowledge Management: 347-356 (CIKM 2018) (CCF B) (Download | DOI)
  • Ji-Rong Wen, Zhicheng Dou*, Ruihua Song: Personalized Web Search. Encyclopedia of Database Systems (2nd ed.) 2018
2017
  • Zhengbao Jiang, Ji-Rong Wen, Zhicheng Dou*, Wayne Xin Zhao, Jian-Yun Nie, Ming Yue. Learning to Diversify Search Results via Subtopic Attention. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017) (CCF A) (Download | DOI)
  • Zhengbao Jiang, Zhicheng Dou*, Ji-Rong Wen. Generating Query Facets Using Knowledge Bases. IEEE Trans. Knowl. Data Eng. 29(2): 315-329 (2017) (TKDE) (CCF A) (Download | DOI)
  • Zhicheng Dou, Zhengbao Jiang, Jinxiu Li, Yichun Zhang, and Ji-Rong Wen. A Method of Mining Query Facets Based on Term Graph Analysis. Chinese Journal of Computer, 2017, 40(3):556-569. (Download | DOI) (In Chinese)
2016
  • Xiaojie Wang, Zhicheng Dou*, Tetsuya Sakai, and Ji-Rong Wen. Evaluating Search Result Diversity using Intent Hierarchies. In Proceedings of SIGIR, 2016. (SIGIR 2016) (CCF A) (Download | DOI)
  • Zhicheng Dou*, Zhengbao Jiang, Sha Hu, Ji-Rong Wen, Ruihua Song: Automatically Mining Facets for Queries from Their Search Results. IEEE Trans. Knowl. Data Eng. (TKDE) 28(2):385-397 (2016) (TKDE) (CCF A) (Download | DOI)
  • Sha Hu, Ji-Rong Wen, Zhicheng Dou, Shuo Shang. Following the dynamic block on the Web. World Wide Web 19(6): 1077-1101 (2016) (Download | DOI)
  • Takehiro Yamamoto, Yiqun Liu, Min Zhang, Zhicheng Dou, Ke Zhou, Ilya Markov, Makoto P. Kato, Hiroaki Ohshima, Sumio Fujita. Overview of the NTCIR-12 IMine-2 Task. NTCIR 2016 (Download)
  • Ming Yue, Zhicheng Dou, Sha Hu, Jinxiu Li, Xiao-Jie Wang, Ji-Rong Wen. RUCIR at NTCIR-12 IMINE-2 Task. NTCIR 2016 (Download)
  • Shaoping Ma, Ji-Rong Wen, Yiqun Liu, Zhicheng Dou, Min Zhang, Yi Chang, Xin Zhao. Information Retrieval Technology - 12th Asia Information Retrieval Societies Conference, AIRS 2016, Beijing, China, November 30 - December 2, 2016, Proceedings. Lecture Notes in Computer Science 9994, Springer 2016, ISBN 978-3-319-48050-3 (Download)
2015
  • Zhongqi Lu, Zhicheng Dou*, Xing Xie, Jianxun Lian, Qiang Yang. Content-based Collaborative Filtering for News Topic Recommendation. In Proceedings of Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015), Austin Texas, USA, Jan 25-29, 2015. (AAAI 2015) (CCF A) (Download | DOI)
  • Sha Hu, Zhicheng Dou*, Xiaojie Wang, Tetsuya Sakai, and Ji-Rong Wen. 2015. Search Result Diversification Based on Hierarchical Intents. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM '15). ACM, New York, NY, USA, 63-72. (CIKM 2015) (CCF B) (Download | DOI)
  • Sha Hu, Zhicheng Dou*, Xiao-Jie Wang, Ji-Rong Wen: Search Result Diversification Based on Query Facets. J. Comput. Sci. Technol. (JCST) 30(4):888-901 (2015) (Download | DOI)
  • Zhicheng Dou and Ji-Rong Wen. Web Analytical Engine in the Big Data Era. Big Data Journal, 2015(3). (In Chinese) (Download | DOI)
↑↑↑↑↑↑After I joined Renmin University of China↑↑↑↑↑↑
2014
  • Yiqun Liu, Ruihua Song, Min Zhang, Zhicheng Dou, Takehiro Yamamoto, Makoto Kato, Hiroaki Ohshima, Ke Zhou. Overview of the NTCIR-11 IMine Task. Proceedings of the 11th NTCIR conference. (Download)
  • Fei Chen, Yiqun Liu, Zhicheng Dou, Keyang Xu, Yujie Cao, Min Zhang, and Shaoping Ma, Revisiting the Evaluation of Diversified Search Evaluation Metrics with User Preferences. Proceedings of the 10th Asia Information Retrieval Society Conference (AIRS 2014) (Download)
  • Jingfei Li, Dawei Song, Peng Zhang, Ji-Rong Wen, and Zhicheng Dou, Personalizing Web Search Results Based on Subspace Projection, Proceedings of the 10th Asia Information Retrieval Society Conference (AIRS 2014) (Download)
  • Shu Tang, Zhicheng Dou, Xing Xie, and Jun He, Detecting and Monitoring Dynamic Content Blocks of a Web Page by Merging its Historical Versions, in SIGIR 2014 Workshop on Temporal, Social and Spatially-aware Information Access (TAIA2014), 2014 (Download)
2013
  • Xiao Ding, Zhicheng Dou, Bing Qin, Ting Liu, and Ji-Rong Wen, Improving Web Search Ranking by Incorporating Structured Annotation of Queries, in Proceedings of EMNLP 2013, pages 468-478, October 2013 (EMNLP 2013) (CCF B) (Download)
  • Kosetsu Tsukuda, Tetsuya Sakai, Zhicheng Dou, and Katsumi Tanaka, Estimating Intent Types for Search Result Diversification, in Information Retrieval Technology, pages 25-37, Springer Berlin Heidelberg, 2013 (Download)
  • Ke Zhou, Tetsuya Sakai, Mounia Lalmas, Zhicheng Dou, and Joemon M. Jose, Evaluating Heterogeneous Information Access, in ACM SIGIR 2013 Workshop on Modeling User Behavior for Information Access Evaluation, 2013 (Download)
  • Qinglei Wang, Yanan Qian, Ruihua Song, Zhicheng Dou, Fan Zhang, Tetsuya Sakai, and Qinghua Zheng, Mining Subtopics from Text Fragments for a Web Query, in Information Retrieval 16(4) pages 484-503, 2013 (Download)
  • Tetsuya Sakai and Zhicheng Dou, Summaries, Ranked Retrieval and Sessions: A Unified Framework for Information Access Evaluation, in Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2013), pages 473-482, ACM, 2013 (The Best Paper Runner-Up Award) (SIGIR 2013) (CCF A) (Download)
  • Tetsuya Sakai, Zhicheng Dou, and Charles L. A. Clark, The Impact of Intent Selection on Diversified Search Evaluation, in Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2013), pages 921-924, ACM, 2013 (SIGIR 2013) (CCF A) (Download)
  • Tetsuya Sakai, Zhicheng Dou, Takehiro Yamamoto, Yiqun Liu, Min Zhang, Makoto Kato, Ruihua Song, and Mayu Iwata, Summary of the NTCIR-10 INTENT-2 Task: Subtopic Mining and Search Result Diversification, in Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2013), pages 761 - 764, ACM, 2013 (SIGIR 2013) (CCF A) (Download)
  • Tetsuya Sakai, Zhicheng Dou, Takehiro Yamamoto, Yiqun Liu, Min Zhang, and Ruihua Song, Overview of the NTCIR-10 INTENT-2 Task, in Proceedings of the 10th NTCIR Conference, pages 94-123, June 18-21, 2013 (Download)
  • Kosetsu Tsukuda, Zhicheng Dou, and Tetsuya Sakai, Microsoft Research Asia at the NTCIR-10 Intent Task, in Proceedings of the 10th NTCIR Conference, June 2013 (Download)
  • Kazuya Narita, Tetsuya Sakai, Zhicheng Dou, and Young-In Song, MSRA at NTCIR-10 1CLICK-2, in Proceedings of the 10th NTCIR Conference, 2013 (Download)
2012
  • Tetsuya Sakai, Zhicheng Dou, Ruihua song, and Noriko Kando, The Reusability of a Diversified Search Test Collection, in Information Retrieval Technology (AIRS 2012), pages 26-38, Springer Berlin Heidelberg, 20 December 2012 (The Best Paper Award) (Download)
2011
  • Zhicheng Dou, Sha Hu, Kun Chen, Ruihua Song, and Ji-Rong Wen, Multi-dimensional Search Result Diversification, in Proceedings of the fourth ACM international conference on Web search and data mining (WSDM 2011), pages 475-484, ACM, February 2011 (WSDM 2011) (CCF B) (Download)
  • Zhicheng Dou, Finding Dimensions for Queries, in Proceedings of the 20th ACM international conference on Information and knowledge management (CIKM 2011), pages 1311-1320, ACM, 2011 (CIKM 2011) (CCF B) (Download)
  • Jialong Han, Qinglei Wang, Naoki Orii, Zhicheng Dou, Tetsuya Sakai, and Ruihua Song, Microsoft Research Asia at the NTCIR-9 Intent Task, in Proceedings of the 10th NTCIR Conference (NTCIR-9), National Institute of Informatics, 2011 (Download)
2010
  • Tetsuya Sakai, Nick Craswell, Ruihua Song, Stephen Robertson, Zhicheng Dou, and Chin-Yew Lin, Simple Evaluation Metrics for Diversified Search Results, in Proceedings of the Third International Workshop on Evaluating Information Access (EVIA), Volumn 26, pages 27, National Institute of Informatics, June 2010 (Download)
  • Ruihua Song, Zhicheng Dou, Hsiao-Wuen Hon, and Yong Yu, Learning Query Ambiguity Models by Using Search Logs, Journal of Computer Science and Technology, 25(4), pages 782-738, Springer, July 2010 (Download)
2009
  • Zhicheng Dou, Kun Chen, Ruihua Song, Yunxiao Ma, Shuming Shi, and Ji-Rong Wen, Microsoft Research Asia at the Web Track of TREC 2009, in Proceedings of TREC 2009, November 2009 (Download)
  • Ji-Rong Wen, Zhicheng Dou, and Ruihua Song, Personalized Web Search, in Encyclopedia of Database Systems, pages 2099-2103, Springer-Verlag, New York, USA, September 2009 (Download)
  • Zhicheng Dou, Ruihua Song, Jian-Yun Nie, and Ji-Rong Wen, Using Anchor Texts with Their Hyperlink Structure for Web Search, in Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval(SIGIR 2009), pages 227-234, ACM, July 2009 (SIGIR 2009) (CCF A) (Download)
  • Zhicheng Dou, Ruihua Song, Ji-Rong Wen, and Xiaojie Yuan, Evaluating the Effectiveness of Personalized Web Search, in IEEE Transactions on Knowledge and Data Engineering (TKDE), 21(8), pages 1178-1190, IEEE computer Society Digital Library, Aug., 2009 (TKDE) (CCF A) (Download)
2008
  • Zhicheng Dou, Ruihua Song, Xiaojie Yuan, and Ji-Rong Wen, Are click-through data adequate for learning web search rankings?, in Proceeding of the 17th ACM conference on Information and knowledge management (CIKM 2008), pages 73-82, ACM, New York, NY, USA, 2008 (CIKM 2008) (CCF B) (Download)
  • Zhicheng Dou, Xiaojie Yuan, and Songbai He, Analysis of Query Repetition in Large-scale Chinese Search Log, Computer Engineering, 34(21), Volumn 21, pages 40-44, 2008 (In Chinese) (Download)
  • Xiaojie Yuan, Zhicheng Dou, Lu Zhang, and Fang Liu, Automatic User Goals Identification Based on Anchor Text and Click-through Data, in Wuhan University Journal of Natural Sciences (WISA2008), 13(4), pages 495-500, 2008 (In Chinese) (Download)
  • Xiaojie Yuan, Zhicheng Dou, Fang Liu, and Lu Zhang, Personalized Web Search Based on Dynamic User Profile, NDBC 2008: Proceedings of the 25th National Database Conference (In Chinese) , 2008 (Download)
  • Lu ZHANG, Xiao-jie YUAN, Fang LIU, and Zhicheng Dou, Research on Distributed Index Mechanism for Large Dataset, Microelectronics & Computer, Volume 10, Pages 037, 2008
2007
  • Zhicheng Dou, Ruihua Song, and Ji-Rong Wen, A large-scale evaluation and analysis of personalized search strategies, in Proceedings of the 16th international conference on World Wide Web (WWW2007), pages 581-590, ACM Press, New York, NY, USA, 2007 (WWW 2007) (CCF A) (Download)

Academic Services

  • Chair/Co-Chair: Sponsorship Co-chair of ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR'18), Program Co-chair (Short Paper) of The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19), General Co-chair of The 12th Asia Information Retrieval Societies Conference (AIRS'16), Program Co-chair of The 13th Asia Information Retrieval Societies Conference (AIRS'17), Chair of Asia Information Retrieval Societies Steering Committee (2018)
  • Area Chair: CCL 2019, NLPCC 2016
  • Senior PC Member: SIGIR (2018-2023)
  • PC Member: SIGIR, KDD, CIKM, WWW, WSDM, IEEE BigData, ICDE, IJCAI, AAAI, EMNLP, NLPCC, CCIR, CCL, CCF BigData, ...
  • Reviewer: TKDE, TOIS, TIST, JMLC, IPM, Neurocomputing, Information Retrieval Journal, DKE, KAIS, JCST, Chinese Journal of Computer
  • Editorial Board: Information Retrieval Journal

Research Projects

Retrieval Augmented Generation

Retrieval-augmented generation (RAG) entails leveraging a retrieval system to augment large language models (LLMs) with pertinent external knowledge, significantly enhancing the quality of text generation. This approach has exhibited substantial efficacy in mitigating the hallucination problem of LLMs, especially in knowledge-intensive tasks such as open-domain question answering. We are working on the following research problems about RAG: (1) Necessity of RAG; (2) Query Understanding; (3) Retrieval and Ranking Models; (4) Key Evidence Extraction; (5) LLM Finetuning and (6) Evaluation and Benchmarking; RAG

  • Recommendation: FlashRAG: A Python Toolkit for Efficient RAG Research. FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 32 pre-processed benchmark RAG datasets and 14 state-of-the-art RAG algorithms. With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components.

Conversational Search

Conversational search systems support multi-turn interaction with users through conversations to meet various information needs. Such a new search paradigm is more natural, convenient, and userfriendly, which is expected to hugely improve users’ search experience in the future. Different from traditional ad-hoc search systems, conversational search systems have to understand the user’s real search intent of the current turn based on the history conversation context, facing a more complex search intent understanding problem. There are two typical ways to implement a conversational search system: (1) Conversational Query Rewriting and (2) Conversational Dense Retrieval.

Personalized and Diversified Search

In recent years, with the quick development of mobile phones and network services, most of our daily life activities have been digitized by various devices and applications. There are more and more user generated personal data available, and with the growth of their scale and variety, how to effectively and efficiently accessing these personal information will become more and more challenging. Various applications including search engines and recommendation engines have been developed to help users access public information in the internet, but how to access all personal data in an integrated way is still under investigation. I believe user-centric personalized and diversified information access will become more and more important in the near future.

Studies show that the vast majority of queries to search engines are short and vague in specifying a user’s intent. Different users may have completely different information needs and goals when using precisely the same query. For example, User A is finding information about Apply Company by issuing a query ``apple'', while User B is finding information related to fruit apple using the same query. When such a query is issued, search engines will return a list of documents that mix different topics. It takes time for a user to choose which information he/she wants, and the search experience in this case is bad. In recent years, I have been working on two different approaches for solving this problem: Personalized Web Search and Search Result Diversification.

  • Personalize Web Search Personalized web search aims to return different search results to different users based upon their interests and preferences. Given the above example query ``query'', a personalized search system tends to just return results about Apple company to user A, or ranks the results about Apple company higher than those about apple fruit. As personalized search will help the user find the information she wants more easily, it is considered as a promising solution to improve the performance of generic web search. My team has designed various personalized search strategies in recent years and advanced the state-of-the-arts in this area.
    • Through a large-scale evaluation and analysis of personalized search strategies (WWW 2007, TKDE 2009), we revealed that personalized search has different impact on different kinds of queries. More specifically, it is effective on queries with large click entropy (likely ambiguious queries), but may hurt queries with small entropy (clear queries).
    • We used hierachical RNN for modeling dynamic user profiles (HRNN:CIKM 2018).
    • We applied generative adversarial networks on personalized search, to solve the problem of limited and noisy click data (PSGAN:SIGIR 2019).
    • More recent works can be found in my publication list......
  • Search Result Diversification：Search Result Diversification is another effective way to solve this problem. Given a query, it aims to provide a list of results that cover as many aspects as possible, so that most users can be satisfied by the top results. Our efforts:
    • Multi-dimensional search result diversification: our multi-dimensional diversification approach generates the best run in TREC 2009 Web Track diversity task.
    • Hierachical search result diversification (Details and Dataset )
    • Search result diversification evaluation (Details and Dataset )
    • Learning to Diversify Search Result via Subtopic Attention (Details and Dataset )
    • To mine better subtopics, we addressed the problem of finding query facets that are multiple groups of words or phrases that explain the underlying information of a query. We have minded query facets by mining and aggregating these significant lists, then used query facets for search result diversification.
    • Getting Ride of Greedy Selection: we propose to take the whole candidate document sequence as input, and simultaneously model the interactions between all candidate documents and directly return their final diversification scores.
    • GIVGAN: we proposed a supervised diversification framework based on Generative Adversarial Network (GAN).
    • More about Search Result Diversification
  • Integrating Personalized Search and Diversified Search: Since both Personalized Search and Search Result Diversification are trying to solve the same query ambiguity problem, it is interesting to study how to integrate them in a search engine. Ideally, users need personalized and diversified results.
  • Integrating Search and Recommendation: In recent years, recommender systems have been widely deployed and used by billions of users. Similar to personalized web search, the goal of a recommender system is to return personalized content (such as news, products, videos, pictures, POIs, etc.) to different users. The main different between search and recommendation is the existence of queries in search. Queries are used by users to describe their information need in a search engine while no explicit queries are required in a recommender system. In fact, in some applications, both search and recommendation functions are provided. The core technology used in two services, especially those deep learning based methods proposed in recent years, are becoming similar. The fundamental problem is: can we use the same model simultaneously for both tasks? I am interested in developing unified user profiling and ranking models for integrating search and recommendation tasks.

Legal AI

The legal domain contains a vast amount of textual data, making it an excellent application area for information retrieval and natural language processing. We are working on (1) Legal Case Retrieval： finding the most relevant cases in a case collection for a given query case and (2) Legal Judgment Prediction: predicting the judgment results of legal cases.

Query Facet Mining

We address the problem of finding multiple groups of words or phrases that explain the underlying query facets, which we refer to as query dimensions/facets. We assume that the important aspects of a query are usually presented and repeated in the query’s top retrieved documents in the style of lists, and query facets can be mined out by aggregating these significant lists.

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