The group recruits Ph.D. and master's students. Interested applicants are welcome to send your CV to jpchen [at] bupt [dot] edu [dot] cn.

Computing resources: the group currently maintains dedicated compute resources and rents additional AutoDL capacity as a buffer resource pool.

The future society is one where data drives intelligent decision-making. Our goal is to make intelligent service carriers such as recommender systems, intelligent retrieval systems, and user profiling systems "smarter" through machine learning and data mining. Targeting personalized recommendation and intelligent decision-making, the group focuses on large models, cross-modal representation learning, and multi-agent collaborative decision-making. We recruit master's and Ph.D. students who are passionate about recommender systems and intelligent data services, with solid programming, English, and mathematical foundations. Research directions center on efficient and explainable recommendation, generalizable personalized recommender systems, and building domain-level open-source data and algorithm platforms — advancing recommendation technology and data intelligence to support national strategic needs in the digital economy and intelligent services.

1. We do not promote "lying flat"; we advocate an "end-to-end" research model from industrial demand to deployment — research that is usable;
2. The group is self-managed by students, advocating a culture of "flexibility and motivation" that balances team atmosphere with individual growth;
3. We encourage master's students to "go out and see the world" after completing two high-quality works; collaborations with research institutes and companies are also welcome;
4. To learn more about the group, please refer to the research directions on this website or contact the faculty directly. Alumni placements can be found on the People page.

PS: Undergraduates who love data mining and research training are welcome to explore frontier scientific questions and prepare for further study.