个人简介

王文杰，男，现任中国科学技术大学信息学院特任教授、博士生导师，国家级青年人才。主要从事个性化大模型和智能体、因果推理、信息检索与推荐、AI4Science等方向的研究。2023年博士毕业于新加坡国立大学，已发表CCF-A类会议或期刊长文60 余篇，累计被引4千余次，获得SIGIR 2024最佳论文提名奖、ACM MM 2019最佳论文提名奖、谷歌博士奖学金等荣誉，相关技术成果落地腾讯、华为等多个商业公司。担任ACM TOIS专刊客座编委，以及TPAMI, TOIS, TKDE, SIGIR, KDD, WWW, NeurIPS, ACL等多个会议/期刊的领域主席/审稿人。

研究方向

• 个性化大模型与智能体

• 信息检索与推荐：搜推大模型、生成式推荐、因果推荐

• AI4Science: Social Science, Bioinformatics

联系方式

办公地点：高新校区一号学科楼A534

电子邮箱：wenjiewang@ustc.edu.cn

个人主页：https://faculty.ustc.edu.cn/wangwenjie/zh_CN/index.htm

代表性论文

[1] Wenjie Wang, Yongfeng Zhang, Tat-Seng Chua. Recommendation in the Era of Generative Artificial Intelligence. <Information Access in the Era of Generative AI>. Editors: Ryen W. White, Chirag Shah. Springer.《生成式AI时代的信息检索》

[2] Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, and Tat-Seng Chua. Diffusion Recommender Model. SIGIR’24高引论文（PaperDigest统计）

[3] Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue. SIGIR’21高引论文

[5] Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, and Tat-Seng Chua. Deconfounded recommendation for alleviating bias amplification. KDD’21高引论文

[6] Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin, and Tat-Seng Chua. Causal representation learning for out-of-distribution recommendation. WWW’22高引论文

[7] Xinyu Lin, Wenjie Wang*(通讯作者), Yongqi Li, Fuli Feng, See-Kiong Ng, and Tat-Seng Chua. Bridging items and language: A transition paradigm for large language model-based recommendation.KDD’24高引论文

[8] Chen Xu, Xiaopeng Ye, Wenjie Wang*(通讯作者), Liang Pang, Jun Xu and Tat-Seng Chua. A Taxation Perspective for Fair Re-ranking, SIGIR’24最佳论文提名奖

[9] Hongru Cai, Yongqi Li, Wenjie Wang*(通讯作者), Fengbin Zhu, Xiaoyu Shen, Wenjie Li, and Tat-Seng Chua. Large language models empowered personalized web agents. WWW’25

[10] Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. RecSys’23最高引论文