連德富

E-Mail:liandefu@ustc.edu.cn

個人主頁:http://staff.ustc.edu.cn/~liandefu 


主要研究方向:大模型檢索增強、大模型智能體、科學智能


連德富,中國科學技術大學計算機學院副院長、教授、博士生導師。他的主要研究方向包括大模型檢索增強、大模型智能體、科學智能等,研發了搜推廣訓推一體化係統Nexus、 通用表征係列模型、GPU原生向量檢索係統Rotlex、工具調用模型ToolACE等,在KDD、ICML、TPAMI、TKDE等CCF-A類會議和期刊發表論文100餘篇。他主持了國自然優青、區域聯合重點、中科院先導A以及企業合作項目等二十餘項,曾獲得省部級自然科學一等獎4項以及國際會議最佳論文獎3項。


獲獎情況

2023 CCF科技成果獎自然科學一等獎(排名第二)

2023 KDD Cup Next Product Recommendation/Generation 兩個賽道分獲季軍和亞軍(全球高校冠軍)

2023 RecSys Challenge 第三名

2022 NeurIPS Traffic4Cast 比賽雙賽道冠軍

2022 中科大招生先進個人

2022 NeurIPS Top Reviewers

2022 WISE Best Paper Award

2021 楊元慶教育基金

2021 中科大招生先進個人

2021 安徽省教學成果獎一等獎(排名第7)

2020 中科大優秀班主任

2019 計算機學院“新創學者”

2018 計算機學院“新創學者”

2018 教育部高等學校科學研究優秀成果獎自然科學一等獎(排名第4)

2018 四川省教學成果獎二等獎(排名第5)

2016 APWeb best student paper runner up


代表性論著

1. Zepu Lu, Jin Chen, Defu Lian*, Zaixi Zhang, Yong Ge and Enhong Chen. Knowledge Distillation for High Dimensional Search Index. The 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), Dec. 2023.

2. Wuchao Li, Chao Feng, Defu Lian*, Yuxin Xie, Haifeng Liu, Yong Ge, and Enhong Chen. Learning Balanced Tree Indexes for Large-Scale Vector Retrieval. The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), pp. 1353-1362, Aug. 2023

3. Chenwang Wu, Xiting Wang, Defu Lian*, Xing Xie, and Enhong Chen. A Causality Inspired Framework for Model Interpretation. The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), pp. 2731-2741, Aug. 2023

4. Chenwang Wu, Defu Lian*, Yong Ge, Zhihao Zhu, and Enhong Chen. Influence-Driven Data Poisoning for Robust Recommender Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(10), 2023

5. Zhaoyi Li, Ying Wei and Defu Lian*. Learning to Substitute Spans towards Improving Compositional Generalization. The 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023), pp. 2791-2811, July 2023.

6. Xu Huang, Defu Lian*, Jin Chen, Liu Zheng, Xing Xie and Enhong Chen. Cooperative Retriever and Ranker in Deep Recommenders. The 32nd Web Conference (WWW 2023), pp. 1150-1161, Apr. 2023.

7. Chao Feng, Wuchao Li, Defu Lian*, Zheng Liu and Enhong Chen. Recommender Forest for Efficient Retrieval. The 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), pp. 38912-38924, Dec. 2022

8. Leyan Deng, Defu Lian*, Chenwang Wu and Enhong Chen. Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy. The 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), pp. 3900-3912, Dec. 2022

9. Jin Chen, Defu Lian*, Yucheng Li, Baoyun Wang, Kai Zheng and Enhong Chen. Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever. The 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), pp. 34817-34830, Dec. 2022

10. Defu Lian, Xing Xie, Enhong Chen and Hui Xiong. Product Quantized Collaborative Filtering. IEEE Transaction on Knowledge and Data Engineering (IEEE TKDE), 33(9), 2021

11. Qi Liu, Jin Zhang, Defu Lian*, Yong Ge, Jianhui Ma and Enhong Chen. Online Additive Quantization. The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), pp. 1098-1108, Aug. 2021.

12. Yongji Wu, Defu Lian*, Neil Gong, Lu Yin, Mingyang Yin, Jingren Zhou and Hongxia Yang. Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation. The 30th Web Conference (WWW 2021), pp. 1262-1273, virtual, Apr. 2021

13. Defu Lian, Yongji Wu, Yong Ge, Xing Xie and Enhong Chen. Geography-Aware Sequential Location Recommendation. The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), pp. 2009–2019, Aug. 2020.

14. Defu Lian, Haoyu Wang, Zheng Liu, Jianxun Lian, Enhong Chen and Xing Xie. LightRec: a Memory and Search-Efficient Recommender System. The 29th Web Conference (WWW 2020), pp. 695–705, Apr. 2020

15. Defu Lian, Qi Liu and Enhong Chen. Personalized Ranking with Importance Sampling. The 29th Web Conference (WWW 2020), pp. 1093–1103, Apr. 2020


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