Shikang(Danny) Wu   /   吴世康

I am a Tech Lead for Douyin Search Ranking, leading the direction of the Douyin Next-Gen Search Engine. We are building a next-generation search engine based on a Generative Search Foundation Model and a Large Recommendation Model.

Before joining Douyin, I worked at Baidu Search as a senior ML scientist. I received my M.S. and B.E. degrees from Beijing Jiaotong University, advised by Zhihao Wu and Youfang Lin.

I am starting to work toward meaningful impact. Feel free to contact me:

Email  /  CV  /  Google Scholar  /  Linkedin

profile photo
Research Interests

I'm interested in recommender systems and information retrieval. More specifically, my research focuses on:

  • Large-scale recommendation models inspired by recent progress in LLMs and VLMs, emphasizing performance improvements through scaling model capacity.
  • The integration of multimodal signals and personalization in recommender systems.
  • Representation learning, particularly graph representation learning.
  • Generative recommendation models, including sequential and multimodal paradigms.
Experience and Education

Experience

  • Jan. 2026 - Present, Tech Lead / Staff ML Scientist - Douyin Search Ranking, Bytedance
    Leading the direction of the Douyin Next-Gen Search Engine. Welcome to join my team! We are building a next-generation search engine based on a Generative Search Foundation Model, a unified model combining retrieval and ranking capabilities, and a Large Recommendation Model scaling towards 4B dense parameters, 100T sparse features, and 100K sequence lengths.
  • Jun. 2021 - Dec. 2023, Senior ML Scientist, Baidu-Search, Baidu
    Received the only cross-level promotion within the Business Group.
  • Jun. 2020 - Sep. 2020, ML Scientist Intern, Meituan

Education

  • 2018.09 - 2021.06, M.E., School of Information Technology, Beijing Jiaotong University
  • 2014.09 - 2018.06, B.E., School of Information Technology, Beijing Jiaotong University
Publications

Note: * indicates equal contributions, † indicates Corresponding authors.

MSN framework
MSN: A Memory-based Sparse Activation Scaling Framework for Large-scale Industrial Recommendation.
Shikang Wu*, Hui Lu*, Jinqiu Jin*, Zheng Chai*, Shiyong Hong, Junjie Zhang, Shanlei Mu, Kaiyuan Ma, Tianyi Liu, Yuchao Zheng, Zhe Wang†, Jingjian Lin
KDD 2026 Accepted
[arXiv]
MDL framework
MDL: A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization.
Shanlei Mu*, Yuchen Jiang*, Shikang Wu*, Shiyong Hong, Tianmu Sha, Junjie Zhang, Jie Zhu, Zhe Chen, Zhe Wang†, Jingjian Lin
KDD 2026 Accepted
[arXiv]
TokenMixer-Large framework
TokenMixer-Large: Scaling Up Large Ranking Models in Industrial Recommenders.
Yuchen Jiang*, Jie Zhu*, Xintian Han*, Hui Lu*, Kunmin Bai*, Mingyu Yang*, Shikang Wu*, Ruihao Zhang*, Wenlin Zhao*, Shipeng Bai, Sijin Zhou, Huizhi Yang, Tianyi Liu, Wenda Liu, Ziyan Gong, Haoran Ding, Zheng Chai, Deping Xie, Zhe Chen, Yuchao Zheng†, Peng Xu
KDD 2026 Accepted
[arXiv]
HyFormer framework
HyFormer: Revisiting the Roles of Sequence Modeling and Feature Interaction in CTR Prediction.
Yunwen Huang*, Shiyong Hong*, Xijun Xiao*, Jinqiu Jin*, Xuanyuan Luo, Zhe Wang, Zheng Chai†, Shikang Wu†, Yuchao Zheng, Jingjian Lin
SIGIR 2026 Accepted
[arXiv]
LEMUR framework
LEMUR: Large scale End-to-end MUltimodal Recommendation.
Xintian Han*, Honggang Chen*, Quan Lin*, Jingyue Gao*, Xiangyuan Ren*, Lifei Zhu, Zhisheng Ye, Shikang Wu, XiongHang Xie, Xiaochu Gan, Bingzheng Wei, Peng Xu, Zhe Wang, Yuchao Zheng†, Jingjian Lin, Di Wu, Junfeng Ge
CIKM 2026 Under Review
[arXiv]

Last update: May 2026      Template