| ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models Q Liu, N Chen, T Sakai, XM Wu WSDM'24, 2024 | 136* | 2024 |
| Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation Q Liu, J Zhu, Q Dai, XM Wu COLING'22, 2022 | 31 | 2022 |
| Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey Q Liu*, J Zhu*, Y Yang, Q Dai, Z Du, XM Wu, Z Zhao, R Zhang, Z Dong KDD'24, 2024 | 23 | 2024 |
| Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation J Wu*, Q Liu*, H Hu, W Fan, S Liu, Q Li, XM Wu, K Tang WWW'25, 2025 | 14 | 2025 |
| CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation J Zhu, M Jin, Q Liu, Z Qiu, Z Dong, X Li RecSys'24, 2024 | 14 | 2024 |
| Discrete Semantic Tokenization for Deep CTR Prediction Q Liu, H Hu, J Wu, J Zhu, MY Kan, XM Wu WWW'24, 2024 | 12 | 2024 |
| Vector Quantization for Recommender Systems: A Review and Outlook Q Liu*, X Dong*, J Xiao, N Chen, H Hu, J Zhu, C Zhu, T Sakai, XM Wu arXiv preprint arXiv:2405.03110, 2024 | 11 | 2024 |
| Continual Graph Convolutional Network for Text Classification T Wu*, Q Liu*, Y Cao, Y Huang, XM Wu, J Ding AAAI'23, 2023 | 10 | 2023 |
| Making Multimodal Generation Easier: When Diffusion Models Meet LLMs X Zhao, B Liu*, Q Liu*, G Shi*, XM Wu ACL'24, 2024 | 8 | 2024 |
| Benchmarking News Recommendation in the Era of Green AI Q Liu*, J Zhu*, Q Dai, XM Wu WWW'24, 2024 | 7 | 2024 |
| Condensing Pre-augmented Recommendation Data via Lightweight Policy Gradient Estimation J Wu, W Fan, S Liu, Q Liu, R He, Q Li, K Tang TKDE'24, 2024 | 6* | 2024 |
| Weak supervision enhanced generative network for question generation Y Wang, J Zheng, Q Liu, Z Zhao, J Xiao, Y Zhuang IJCAI'19, 2019 | 6 | 2019 |
| Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning Y Luo, H Li, Q Liu, L Shi, XM Wu ICLR'25, 2025 | 5* | 2025 |
| AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment N Chen, J Liu, X Dong, Q Liu, T Sakai, XM Wu SIGIR-AP'24, 2024 | 5 | 2024 |
| FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation Q Liu, J Zhu, J Wu, T Wu, Z Dong, XM Wu WWW'23, 2023 | 5 | 2023 |
| STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM Q Liu, J Zhu, L Fan, Z Zhao, XM Wu arXiv preprint arXiv:2409.07276, 2024 | 4 | 2024 |
| Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision H Hu, Q Liu, C Li, MY Kan ECIR'24, 2024 | 4 | 2024 |
| Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization Q Liu, L Fan*, J Xiao*, J Zhu, XM Wu WWW'24, 2023 | 3 | 2023 |
| Legommenders: A Comprehensive Content-Based Recommendation Library with LLM Support Q Liu, L Fan, XM Wu WWW'25, 2025 | 1 | 2025 |
| Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling J Wu, W Fan, S Liu, Q Liu, Q Li, K Tang arXiv preprint arXiv:2309.12723, 2023 | 1 | 2023 |
Xiao-Ming WuAssociate Professor, The Hong Kong Polytechnic UniversityEmail được xác minh tại polyu.edu.hk