Scholar
Langming Liu
Google Scholar ID: kfNSEdQAAAAJ
PhD, City University of Hongkong
Citations & Impact
Citations
104
H-index
4
i10-index
1
Publications
9
Co-authors
3
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Unlocking Scaling Law in Industrial Recommendation Systems with a Three-step Paradigm based Large User Model. KDD, 2026 (CCF-A Conference, Under Review). https://arxiv.org/abs/2502.08309
- How to inject knowledge efficiently? Knowledge Infusion Scaling Law for Pre-training Large Language Models. EMNLP, 2025 (CCF-B Conference, Accepted). https://arxiv.org/abs/2509.19371
- Benchmarking Large Language Models in E-commerce Leveraging Knowledge Graph. CIKM, 2025 (CCF-B Conference, Accepted). https://arxiv.org/abs/2503.15990
- ChineseEcomQA: A Scalable E-commerce Concept Evaluation Benchmark for Large Language Models. KDD, 2025 (CCF-A Conference, Accepted). https://arxiv.org/abs/2502.20196
- UQABench: Evaluating User Embedding for Prompting LLMs in Personalized Question Answering. KDD, 2025 (CCF-A Conference, Accepted). https://arxiv.org/abs/2502.19178
- Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems. KDD, 2025 (CCF-A Conference, Accepted). https://arxiv.org/abs/2506.23090
- Efficient and Robust Regularized Federated Recommendation. CIKM, 2024 (CCF-B Conference, Accepted). https://arxiv.org/abs/2411.01540
- Analysis of Regularized Federated Learning. Neurocomputing (SCI-Q2 Journal, Accepted). https://arxiv.org/abs/2411.01548
- Deep Learning for Social Recommendation: A Survey. TOIS, 2023 (CCF-A Journal, Under Review).
- LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems. SIGIR, 2023 (CCF-A Conference, Accepted). https://arxiv.org/abs/2411.01537
Research Experience
- Working as a senior algorithm engineer at Taobao & Tmall Group of Alibaba.
Background
- Currently working as a senior algorithm engineer at Taobao & Tmall Group of Alibaba. Research interests include Large Language Models, Recommendation Systems, Federated Learning, and Reinforcement Learning.
