BRIEF INTRODUCTION

吴李康 博士，于2024年6月获得中国科学技术大学计算机应用技术专业博士学位，于2024年秋季学期加入天津大学管理与经济学部，担任信息管理与系统系讲师展开大数据管理交叉研究。近5年来，在国内外数据管理与挖掘和人工智能领域的顶级会议和期刊上发表论文40多篇，其中包括20余篇一作、通讯论文（含共一），单篇最高引600多次，例如IEEE TKDE、TEVC、Scientific Report、Tourism Management、Pattern Recognition、ACM TKDD、IP&M、ICML、ACM SIGKDD、AAAI、IJCAI、ACM SIGIR等CCF推荐A类期刊会议、管理科学ABS高质量期刊、或中科院高分区SCI期刊。担任IEEE TPAMI、IEEE TKDE、ICML、ACM SIGKDD、ACM SIGIR、NurIPS、ICLR等多个高水平期刊及会议审稿人、Area Chair 或PC。研究工作主要集中在大模型复杂决策、数据挖掘与信息管理学科的交叉领域，利用大模型技术、图表示学习算法和知识图谱分析市场行为数据，为管理决策提供有效支持，并且具有丰富的工业界合作交流经历，如联想、小米、快手、蚂蚁金服、BOSS直聘等，申请和授权国家发明专利10多项。于2023年6月发布大语言模型应用在推荐系统中的首篇综述，提出的三种LLM4Rec（LLM4RS）建模范式在业界取得了广泛的影响。入选2025中国人工智能学会-社会计算新星，荣获机器学习与数据挖掘领域国际顶级竞赛KDD CUP 2019特等奖，金融时间序列建模相关研究成果入选CCF A类中文期刊《计算机研究与发展》“2019高引论文Top 10奖”及“ESI高被引论文”。欢迎学界、业界同仁共同开展产学研合作。Google Scholar主页

RESEARCH AREA

研究方向

1、数据挖掘
2、图结构数据表征学习
3、大语言模型决策应用 4、Agentic RL及自主进化 5、推荐系统

教授课程

RESEARCH ACHIEVEMENTS

部分期刊论文

[1] 金宗瀚, 吴李康, 刘玉祥, 何明, 赵洪科, 基于知识增强的智能体复杂任务分解方法, 控制与决策，录用待发表. (中文核心, FMS T1类期刊)

[2] Hongke Zhao, Zhicheng Xiang, Likang Wu, Beyond Popularity Bias in E-commerce: Aligning Collaborative Knowledge with Large Language Models via Causal Intervention for Robust Recommendations, Decision Support Systems (DSS), Accepted. 2026. (ABS 3, FMS B类期刊)

[3] Lei Zhang, Meng Zhu, Yuanyuan Ge, Haipeng Yang, Likang Wu, Hongke Zhao, Multi-Party Multi-Objective Optimization for Discrete Problems: A Case Study on Multi-Stakeholder Recommendation, IEEE Transactions on Evolutionary Computation (IEEE TEVC), Accept, 2025. (IF:11.169, ABS 4)

[4] Yuanyuan Ge, Likang Wu, Haipeng Yang, Fan Cheng, Hongke Zhao, Lei Zhang, MORA-LLM: Enhancing Multi-Objective Optimization Recommendation Algorithm by Integrating Large Language Models, IEEE Transactions on Evolutionary Computation (IEEE TEVC), Accepted. (IF:11.169, ABS 4)

[5] Wenhui Liu, Likang Wu, Hongke Zhao(赵洪科)*, Improving Sentiment Analysis in Tourism through LLM-Enhanced Irony Detection, Tourism Management(TM), 112:105272, 2026. (JCR Q1, ABS 4, FMS A类期刊, IF:12.4)

[6] Hongke Zhao, Yuanpei Sui, Likang Wu. Exploring the interpretability of vision GCN with a multi-method approach. Expert Systems with Applications, 127624. (FMS C类期刊)

[7] Lei Zhang, Wuji Zhang, Likang Wu, Hongke Zhao. GCTN: Graph Competitive Transfer Network for Cross-Domain Multi-Behavior Prediction. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE). (IF:6.97, JCR Q1, CCF A类期刊)

[8] Lei Zhang, Mingren Ke, Likang Wu, Wuji Zhang, Zihao Chen, Hongke Zhao. Multi-Objective Graph Contrastive Learning for Recommendation. IEEE Transactions on Big Data (IEEE TBD). (CCF C类期刊)

[9] Jun Wang, Likang Wu, Qi Liu, Yu Yang. An efficient continuous control perspective for reinforcement-learning-based sequential recommendation. Knowledge-Based Systems.

[10] Jiang, Junji, Likang Wu, Zhipeng Hu, Runze Wu, Xudong Shen, and Hongke Zhao. "Knowledge enhanced graph contrastive learning for match outcome prediction." Information Processing & Management 62, no. 3 (2025): 104010. (ABS2)

[11] Lv, Bing, Junji Jiang, Likang Wu, and Hongke Zhao. "Team formation in large organizations: A deep reinforcement learning approach." Decision Support Systems 187 (2024): 114343. (ABS3)

[12] Qingyang Mao, Zhi Li, Likang Wu, Qi Liu, Promoting Machine Abilities of Discovering and Utilizing Knowledge in a Unified Zero-shot Learning Paradigm, ACM Transactions on Knowledge Discovery from Data (TKDD), 19(1), 1-26, 2024. (IF4.16, JCR Q1, CCF/FMS B类期刊)

[13] Bin Feng, Shulan Ruan, Likang Wu, Huijie Liu, Kai Zhang, Kun Zhang, Qi Liu, Enhong Chen, Caption matters: a new perspective for knowledge-based visual question answering, Knowledge and Information Systems (KAIS), 66(11), 6975-7003, 2024

[14] Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, Enhong Chen, A survey on large language models for recommendation, World Wide Web, 27(5), 60.

[15] Ping Ni, Xianquan Wang, Bing Lv, Likang Wu, GTR: An Explainable Graph Topic-Aware Recommender for Scholarly Document, Electronic Commerce Research and Applications (ECRA), 101439, 2024. (ABS2)

[16] 王慜懋，赵洪科，吴李康，焦之贤，黄振亚，语言模型增强的引文网络连边因子挖掘，大数据，11(2):91-106，2025. (CCF T2类期刊)

[17] Likang Wu, Zhi Li, ​Hongke Zhao, Zhenya Huang, Yongqiang Han, Junji Jiang, Enhong Chen, Supporting Your Idea Reasonably: A Knowledge-Aware Topic Reasoning Strategy for Citation Recommendation, IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 36(8):4275-4289, 2024. (IF:6.97, JCR Q1, CCF A类期刊)

[18] Likang Wu, Hongke Zhao, Zhi Li, Zhenya Huang, Qi Liu, Enhong Chen. 2023. Learning the Explainable Semantic Relations via Unified Graph Topic-Disentangled Neural Networks. ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 17.8 (2023): 1-23. (CCF B Journal)

[19] Likang Wu, Zhi Li, Hongke Zhao, Qi Liu, Enhong Chen. 2022. Estimating fund-raising performance for start-up projects from a market graph perspective. Pattern Recognition, 121 (2022): 108204. (JCR Q1, CCF B Journal)

[20] Junji Jiang, Likang Wu (Co-first author), Hongke Zhao, Hengshu Zhu, Wei Zhanga. 2023. Forecasting movements of stock time series based on hidden state guided deep learning approach. Information Processing & Management (IPM), 60.3 (2023): 103328. (JCR Q1, CCF B, ESI高被引论文)

[21] Hongke Zhao, Likang Wu (First student author) , Zhi Li, Xi Zhang, Qi Liu, Enhong Chen, et al. 2019. Predicting the Dynamics in Internet Finance Based on Deep Neural Network Structure. Journal of Computer Research and Development, 56.8 (2019): 1621-1631. Top 10 Papers Highly Cited Award in Journal of Computer Research and Development (CCF Chinese A Journal)

[22] Lei Zhang, Wuji Zhang, Likang Wu, Ming He, Hongke Zhao. 2023. HGCN: Socially Enhanced Heterogeneous Graph Convolutional Network for Multi-Behavior Prediction. ACM Transactions on the Web 18.1 (2023): 1-27. (CCF B Journal)

[23] Jiahui Wang, Likang Wu, Hongke Zhao, Ning Jia. 2023. Multi-view enhanced zero-shot node classification. Information Processing & Management, 60.6 (2023): 103479. (JCR Q1, CCF B Journal).

部分会议论文

[1] Minmao Wang, Xingchen Liu, Shijie Yi, Likang Wu, Hongke Zhao, Fei Pan, Qingpeng Cai, and Peng Jiang. Hierarchical Semantic RL: Tackling the Problem of Dynamic Action Space for RL-based Recommendations. In Proceedings of the ACM Web Conference 2026 (WWW '26), Accepted. (CCF A类会议)

[2] Jiale Huang, Yinyuan Xiao, Likang Wu, Yuanguang Zheng, and Hongke Zhao. Dynamic Routing-Based Adaptive Multi-LLM Collaboration: A Unified Recommendation Framework with Decision Knowledge Complementation. In Proceedings of the ACM Web Conference 2026 (WWW '26), Accepted. (CCF A类会议)

[3] Minmao Wang, Rui Zhang, Shijie Yi, Qingpeng Cai, Likang Wu, Hongke Zhao, Fei Pan and Peng Jiang, TemporalExpertNet: Cross-Temporal Knowledge Reuse for Promotion-Aware CVR Prediction, In Proceedings of the 19-th International Conference on Web Search and Data Mining (WSDM'26), Boise, Idaho, USA, Accepted. (CCF B类会议)

[4] Zhong Guan, Likang Wu, Hongke Zhao, Ming He, Jianping Fan, Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data, Forty-Second International Conference on Machine Learning (ICML'25), Vancouver, Canada, Accepted. (CCF A类会议)

[5] Zipeng Liu, Likang Wu (Co-first author), Ming He, Zhong Guan, Hongke Zhao, Nan Feng, Multi-View Empowered Structural Graph Wordification for Language Models, 39th AAAI Conference on Artificial Intelligence (AAAI'25), Philadelphia, Pennsylvania, USA, 39(23), 24714-24722, 2025 (CCF A类会议)

[6] Zhong Guan, Likang Wu, Hongke Zhao, Ming He, Jianping Fan, Enhancing collaborative semantics of language model-driven recommendations via graph-aware learning, IEEE Transactions on Knowledge and Data Engineering, 2025 (CCF A)

[7] Xianquan Wang, Likang Wu, Zhi Li, Haitao Yuan, Shuanghong Shen, Huibo Xu, Yu Su. Mitigating Redundancy in Deep Recommender Systems: A Field Importance Distribution Perspective. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025. (CCF A类会议)

[8] Liping Liu, Chunhong Zhang, Likang Wu, Chuang Zhao, Zheng Hu, Ming He, Jianping Fan. Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction. NAACL 2025.

[9] Yanmin Dong, Zhenya Huang, Guanhao Zhao, Hongke Zhao, Likang Wu, Binbin Jin, Yixia Zhao, Qi Liu. Enhancing Code Search Intent with Programming Context Exploration. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (WSDM'25), Hannoverr, Germany, 596-605, 2024. (CCF B类会议)

[10] Runqi Yang, Liu Yu, Zhi Li, Shaohui Li, and Likang Wu. "Rethinking Offline Reinforcement Learning for Sequential Recommendation from A Pair-Wise Q-Learning Perspective." In 2024 International Joint Conference on Neural Networks (IJCNN), pp. 1-9. IEEE, 2024.

[11] Xianquan Wang, Likang Wu, Shukang Yin, Zhi Li, Yanjiang Chen, hufeng, Yu Su, Qi Liu, I-AM-G: Interest Augmented Multimodal Generator for Item Personalization, EMNLP 2024

[12] Zhijun Dong, Likang Wu (Co-first author), Kai Zhang, Ye Liu, Zhi Li, Hongke Zhao, Enhong Chen, FZR: Enhancing Knowledge Transfer via Shared Factors Composition in Zero-Shot Relational Learning, In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM '2024). (CCF B Conference)

[13] Likang Wu, Zhaopeng Qiu, Zhi Zheng, Hengshu Zhu, Enhong Chen. Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI '24). (CCF A Conference)

[14] Likang Wu, Zhi Li, Hongke Zhao, Zhefeng Wang, Qi Liu, Baoxing Huai, Nicholas Jing Yuan, and Enhong Chen. 2023. Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23), pp. 2618–2628. (CCF A Conference)

[15] Likang Wu, Junji Jiang, Hongke Zhao, Hao Wang, et al. 2023. KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI '23), pp. 2361-2369 (CCF A Conference)

[16] Likang Wu, Hao Wang, Enhong Chen, Zhi Li, Hongke Zhao, Jianhui Ma. 2022. Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22), pp. 2704-2708. (CCF A Conference)

[17] Likang Wu, Zhi Li, Hongke Zhao, Zhen Pan, Qi Liu, Enhong Chen. 2020. Estimating Early Fundraising Performance of Innovations via Graph-Based Market Environment Model. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI '20), pp. 6396-6403. (CCF A Conference)

[18] Likang Wu, Zhi Li, Hongke Zhao, Qi Liu, Mengdi Zhang, Enhong Chen. 2021. Learning the Implicit Semantic Representation on Graph-Structured Data. In Proceedings of the Database Systems for Advanced Applications (DASFAA '21), Part I 26, pp. 3-19. (CCF B Conference)

[19] Yang Yu, Qi Liu, Likang Wu, Runlong Yu, Lei Yu, Zaixi Zhang. 2023. Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI '23), pp. 4854-4863. (CCF A Conference)

[20] Yongqiang Han, Hao Wang, Kefan Wang, Likang Wu, Zhi Li, Wei Guo, Yong Liu, Defu Lian, Enhong Chen, Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation, Proceedings of the ACM on Web Conference 2024 (WWW 2024). (CCF A Conference)

[21] Qingyang Mao, Qi Liu, Zhi Li, Likang Wu, Bing Lv, Zheng Zhang, Cross-reconstructed Augmentation for Dual-target Cross-domain Recommendation, Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024). (CCF A Conference)

[22] Yixiao Ma, Shiwei Tong, Ye Liu, Likang Wu, et al. 2021. Enhanced Representation Learning for Examination Papers with Hierarchical Document Structure. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21), pp. 2156-2160. (CCF A Conference)

[23] Zhi Li, Bo Wu, Qi Liu, Likang Wu, Hongke Zhao, Tao Mei. 2020. Learning the Compositional Visual Coherence for Complementary Recommendations. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI '20), pp. 3536–3543. (CCF A Conference)

[24] Yongqiang Han, Likang Wu, Hao Wang, Guifeng Wang, Mengdi Zhang, Zhi Li, Defu Lian, et al. 2023. GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation. In Proceedings of the Database Systems for Advanced Applications (DASFAA '23), pp. 286-296. (CCF B Conference)

[25] Zhikang Mo, Qixiang Shao, Likang Wu, Runlong Yu, Qi Liu, J Xu, Hongmei Song, Enhong Chen. 2022. TGNN: A GNN-Based Method with Multi-entity Node for Personal Banking Time Prediction. In Proceedings of the Conference on Information Technology and Nanotechnology (CINT '22), pp. 68-79.

[26] Mingjia Yin, Hao Wang, Xiang Xu, Likang Wu, Sirui Zhao, Wei Guo, Yong Liu, Ruiming Tang, Defu Lian and Enhong Chen. 2023. APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '2023), pp. 3009-3019. (CCF B Conference)

咨政研究/行业研究/标准研究

AWARDS

2025中国人工智能学会社会计算新星学者 (2025.08)

博士研究生国家奖学金 (2023.10)

中国科大环球数码奖学金 (2022.12)

《计算机研究与发展》(CCF A 中文期刊) 2019年TOP 10高引用论文 (2020.12)

硕士研究生国家奖学金 (2020.10)

KDD CUP 2019 Regular ML Track PaddlePaddle 特等奖 (2019.08)

硕士/博士研究生一等奖学金 (2018.10 - 2022.10)

2018 快手用户兴趣建模大赛优胜奖 (Top 10) (2018.08)

ACM/ICPC 大学生程序设计竞赛亚洲区域赛铜牌，全国邀请赛二等奖 (2016.10)

本科生一等奖学金 (2014.10 - 2017.10)

本科生国家奖学金 (2015.10)

PROJECTS

国家自然科学基金青年项目（C）面向推荐领域的大模型复杂意图挖掘研究与应用 (2026-2028， 主持人)

天津大学自主创新项目-社会影响力 (2025-2026， 主持人)

智能互联系统安徽省实验室自主创新项目 (2025-2027， 主持人)

天津市教委社科重大项目子课题，(2024-2027， 主持人)

中国人工智能学会 CAAI-联想蓝天科研基金，AI Agent中复杂任务的可控性分解 (2024-2025， 主持人)

天津市自然科学基金-青年项目-大模型驱动的金融市场动态分析与应用研究 (2024-2026， 主持人)

基于图挖掘的相关应用，横向项目，合作单位：美团点评，华为诺亚 (2021 - 2023， 参与)

• 研究内容: 根据搜推业务需求，设计并开发了基于user-item网络和item-item网络的无监督推荐框架。在此基础上，对于item的文本内容，引入知识图谱进一步挖掘了基于知识主题建模的细粒度语义特征，较大幅度提高了推荐的准确性以及结果的泛化性和多样性，从而适应跨领域的推荐任务。

科技部国家重点研发计划大数据知识工程，基于情景感知的知识导航 (2019.08 - 2021.12, 参与)

• 研究内容: 情景知识中隐含用户需求，通过挖掘情景知识深入理解用户意图，结合医疗场景中的独特领域知识实现医疗知识导航

RESEARCH BACKGROUND

1. 2018/09-2024/06，中国科学技术大学，计算机科学与技术，博士;
2. 2014/09-2018/06，西北农林科技大学，本科；

SERVICES

Conference Program Committee (PC):
NeurIPS 2024, ICLR 2024, AAAI 2024, KDD 2024, WWW 2024, SIGIR 2024, WWW 2024, IJCAI 2024, KDD 2023, SIGIR 2023, IJCAI 2023, CICAI 2022, etc

Journal Reviewer:
IEEE TPAMI, IEEE TKDE, ACM TKDD, ACM TOIS, IEEE TNNLS, IEEE TKDE, IEEE TNNLS, IPM, Frontiers of Computer Science, etc