| Contrastive meta learning with behavior multiplicity for recommendation W Wei, C Huang, L Xia, Y Xu, J Zhao, D Yin WSDM 2022, Best Paper Candidate, 1120-1128, 2022 | 127 | 2022 |
| Heterogeneous graph contrastive learning for recommendation M Chen, C Huang, L Xia, W Wei, Y Xu, R Luo Proceedings of the sixteenth ACM international conference on web search and …, 2023 | 87 | 2023 |
| LLMRec: Large Language Models with Graph Augmentation for Recommendation W Wei, X Ren, J Tang, Q Wang, L Su, S Cheng, J Wang, D Yin, C Huang WSDM 2024, 2023 | 69 | 2023 |
| Multi-Modal Self-Supervised Learning for Recommendation W Wei, C Huang, L Xia, C Zhang Proceedings of the ACM Web Conference 2023, 2023 | 68 | 2023 |
| Graphgpt: Graph instruction tuning for large language models J Tang, Y Yang, W Wei, L Shi, L Su, S Cheng, D Yin, C Huang SIGIR'2024, 2023 | 67 | 2023 |
| Representation learning with large language models for recommendation X Ren, W Wei, L Xia, L Su, S Cheng, J Wang, D Yin, C Huang WWW 2024, 2023 | 38 | 2023 |
| SSLRec: A Self-Supervised Learning Library for Recommendation X Ren, L Xia, Y Yang, W Wei, T Wang, X Cai, C Huang WSDM 2024, 2023 | 11* | 2023 |
| Higpt: Heterogeneous graph language model J Tang, Y Yang, W Wei, L Shi, L Xia, D Yin, C Huang arXiv preprint arXiv:2402.16024, 2024 | 9 | 2024 |
| Multi-relational contrastive learning for recommendation W Wei, L Xia, C Huang Proceedings of the 17th ACM Conference on Recommender Systems, 338-349, 2023 | 9* | 2023 |
| Graphedit: Large language models for graph structure learning Z Guo, L Xia, Y Yu, Y Wang, Z Yang, W Wei, L Pang, TS Chua, C Huang arXiv preprint arXiv:2402.15183, 2024 | 7 | 2024 |
| PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning W Wei, J Tang, Y Jiang, L Xia, C Huang WWW 2024, 2024 | 4 | 2024 |
Chao HuangAssistant Professor of Computer Science, University of Hong Kong在 hku.hk 的电子邮件经过验证
