赵苡积

研究领域

主要研究方向为时空智能及其在智慧城市等领域的应用，具体研究兴趣包括时空数据挖掘、时空基础模型等。

研究概况

2023年博士毕业于北京交通大学计算机科学与技术专业，同年入职williamhill官网，入选“williamhill官网青年人才培优计划”。发表学术论文20余篇，其中以第一作者/通讯身份发表IEEE/ACM Trans.系列期刊论文6篇。获云南省自然科学一等奖1项，曾在多项国际赛事中获奖。担任IEEE TKDE、IEEE TNNLS、IEEE TITS、NeurIPS、ICLR、KDD、AAAI、IJCAI等多种国际期刊和会议审稿人。个人主页：yijizhao.github.io.

奖励与荣誉

云南省自然科学一等奖，社交媒体数据的关联关系挖掘、知识表示与协同推荐，排名：7/7，2025.

KDD CUP国际数据挖掘竞赛，季军（3/2490），2022.

IKCEST CUP国际大数据竞赛，一等奖（2/2312），2019.

学术成果

一、代表性成果：

1. [IEEE TII] S. Qin, Y. Zhao*, H. Wu*, L. Zhang, Q. He. Harnessing the power of large language model for effective web api recommendation. IEEE Transactions on Industrial Informatics (TII), 2025, 21(7): 5360-5370.

2. [IEEE TSC] H. Wu, S. Tian, B. Jin, Y. Zhao*, L. Zhang*. Effective graph modeling and contrastive learning for time-aware QoS prediction. IEEE Transactions on Services Computing (TSC), 2024, 17(6): 3513-3526.

3. [IEEE TNNLS] Y. Fang, H. Wu*, Y. Zhao*, L. Zhang, S. Qin, X. Wang. Diversifying collaborative filtering via graph spreading network and selective sampling, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024, 35(10): 13860-13873. 

4. [IEEE TITS] Y. Zhao, Y. Lin, H. Wen, T. Wei, X. Jin, H. Wan*. Spatial-temporal position-aware graph convolution networks for traffic flow forecasting, IEEE Transactions on Intelligent Transportation Systems (TITS), 2023, 24(8): 8650–8666.

5. [IEEE TITS] Y. Zhao, Y. Lin, Y. Zhang, H. Wen, Y. Liu, H. Wu, Z. Wu*, S. Zhang, H. Wan. Traffic inflow and outflow forecasting by modeling intra- and inter-relationship between flows, IEEE Transactions on Intelligent Transportation Systems (TITS), 2022, 23(11): 20202–20216.

6. [ACM TWEB] Y. Zhao, Y. Lin, Z. Wu*, Y. Wang, H. Wen. Context-aware distance measures for dynamic networks, ACM Transactions on the Web (TWEB), 2022, 16(1): 1–34. 

二、其他合著成果：

1. [IJCAI 2025] Y. Ge, J. Li, Y. Zhao, H. Wen, Z. Li, M. Qiu, H. Li, M. Jin, S. Pan. T2s: High-resolution time series generation with text-to-series diffusion models, Proc. IJCAI, 2025.

2. [IEEE TSC] J. Li, H. Wu*, Q. He, Y. Zhao, X. Wang. Dynamic QoS prediction with intelligent route estimation via inverse reinforcement learning, IEEE Transactions on Services Computing (TSC), 2024, 17(2): 509-523.

3. [IEEE TETCI] Q. Tang, Y. Zhao, H. Wu*, L. Zhang. Node clustering on attributed graph using anchor sampling strategy and debiasing strategy, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 2024, 8(4): 3017-3028.

4. [IEEE TKDE] H. Wen, Y. Lin, L. Wu, X. Mao, T. Cai, Y. Hou, S. Guo, Y. Liang, G. Jin, Y. Zhao, R. Zimmermann, J. Ye, H. Wan*. A survey on service route and time prediction in instant delivery: Taxonomy, progress, and prospects, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024, 36(12): 7516-7535.

5. [KBS] T. Wei, Y. Lin, S. Guo, Y. Lin, Y. Zhao, X. Jin, Z. Wu, H. Wan*. Inductive and adaptive graph convolution networks equipped with constraint task for spatial–temporal traffic data kriging, Knowledge-Based Systems (KBS), 2024, 284: 111325.

6. [ESWA] X. Jin, J. Wang, S. Guo, T. Wei, Y. Zhao, Y. Lin, H. Wan*. Spatial–temporal uncertainty-aware graph networks for promoting accuracy and reliability of traffic forecasting, Expert Systems with Applications (ESWA), 2024, 238: 122143.

7. [IEEE TNSM] H. Lian, J. Li, H. Wu*, Y. Zhao, L. Zhang, X. Wang. Toward effective personalized service QoS prediction from the perspective of multi-task learning, IEEE Transactions on Network and Service Management (TNSM), 2023, 20(3): 2587–2597.

8. [KBS] Q. Tang, Y. Zhao, H. Wu*, L. Zhang. Adversarial cluster-level and global-level graph contrastive learning for node representation, Knowledge-Based Systems (KBS), 2023, 279: 110935.

9. [KDD 2022] H. Wen, Y. Lin, X. Mao, F. Wu, Y. Zhao, H. Wang, J. Zheng, L. Wu, H. Hu, H. Wan*. Graph2Route: A dynamic spatial-temporal graph neural network for pick-up and delivery route prediction, Proc. KDD, 2022: 4143–4152.