I lead the USTC Lab for Data Science. My research interests span information retrieval, data mining, and multi-media analytics. I have over 100 publications appeared in several top conferences such as SIGIR, WWW, and KDD, and journals including TKDE, TOIS, and TNNLS. My work on recommender systems has received the Best Paper Award Honourable Mention in SIGIR (2021, 2016) and WWW (2018). Moreover, I have served as the Associate Editor for journals including ACM TOIS, IEEE TBD, etc., and (senior) PC member for conferences including SIGIR, WWW, KDD, MM, etc.

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1. Hiring tenure-track faculties and postdocs in NLP/IR/DM. Requirements:
- With PhD degree (or graduate soon)
- At least three first-author papers on top-tier conferences/journals
We provide competitive salary, sufficient funding and student supports, and good career opportunities.

2. Hiring PhD students from USTC and masters. Requirements:
- Strong code ability (C/C++ or Python)
- English (CET-6 score 500+, or equal levels)
- Determination to do high-quality research.

3. We recently release five surveys on different aspects (causal, bias, conversational, neural, gnn) of Recsys:
- Causal Inference in Recommender Systems: A Survey and Future Directions
- Bias and Debias in Recommender System: A Survey and Future Directions
- Advances and Challenges in Conversational Recommender Systems: A Survey
- A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation
- Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

4. We recently release an outlook paper on large language models (LLMs) for information retrieval, as an output of Chinese IR community's strategic thinking:
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community