|
2027 Journal Article Handling multiple, dynamic, and interactive personas in LBSNs with graphs of agentsDoan, Viet Hung, Pham, Trinh, Nguyen, Quoc Viet Hung, Yin, Hongzhi, Jo, Jun and Nguyen, Thanh Tam (2027). Handling multiple, dynamic, and interactive personas in LBSNs with graphs of agents. Information Sciences, 759 124043, 1-20. doi: 10.1016/j.ins.2026.124043 |
|
2026 Journal Article A survey of social network simulation in the LLM era: From classical models to Generative AgentsNguyen, Thanh Tam, Pham, Trinh, Huynh, Viet, Nguyen, Minh Hieu, Vo, Bay, Qu, Liang, Li, Jianxin, Yin, Hongzhi and Nguyen, Quoc Viet Hung (2026). A survey of social network simulation in the LLM era: From classical models to Generative Agents. Knowledge-Based Systems, 352 116947, 116947-352. doi: 10.1016/j.knosys.2026.116947 |
|
2026 Journal Article RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language AgentsWang, Zongwei, Gao, Min, Yu, junliang, Hou, Yupeng, Sadiq, Shazia and Yin, Hongzhi (2026). RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents. ACM Transactions on Information Systems 3841469. doi: 10.1145/3841469 |
|
2026 Conference Publication LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly DetectionWang, Dezheng, Chen, Tong, Pang, Guansong, Chen, Congyan, Li, Shihua and Yin, Hongzhi (2026). LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection. New York, NY, USA: ACM. doi: 10.1145/3770855.3818044 |
|
2026 Conference Publication Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-LearningPham, Trinh, Huynh, Viet, Yin, Hongzhi, Nguyen, Quoc Viet Hung and Nguyen, Thanh Tam (2026). Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning. New York, NY, USA: ACM. doi: 10.1145/3770855.3818104 |
|
2026 Journal Article Efficient prompt learning for traffic forecastingZhang, Qianru, Gao, Xinyi, Zhou, Alexander, Cheng, Reynold, Yiu, Siu-Ming and Yin, Hongzhi (2026). Efficient prompt learning for traffic forecasting. VLDB Journal, 35 (5) 45. doi: 10.1007/s00778-026-00983-7 |
|
2026 Conference Publication LLM-UP: SIGIR 2026 Workshop on LLM-powered User Profiling for Search and RecommendationYin, Hongzhi, Yuan, Wei, Zhang, Yi, Mackenzie, Joel, Nguyen, Quoc Viet Hung, Zhao, Wayne Xin, Li, Yong and Yao, Lina (2026). LLM-UP: SIGIR 2026 Workshop on LLM-powered User Profiling for Search and Recommendation. New York, NY, USA: ACM. doi: 10.1145/3805712.3808652 |
|
2026 Conference Publication Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender SystemsZhao, Yuchuan, Chen, Tong, Yu, Junliang, Wang, Zongwei, Cui, Lizhen and Yin, Hongzhi (2026). Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems. New York, NY, USA: ACM. doi: 10.1145/3805712.3809691 |
|
2026 Conference Publication ProEchoMem: enhancing long video understanding via multi-trace probe-echo memoryXu, Derong, Chen, Yanxin, Wang, Wanyu, Jia, Pengyue, Zhang, Chao, Wang, Maolin, Wang, Yiqi, Qiang, Jipeng, Wei, Xuetao, Yin, Hongzhi, Xu, Tong and Zhao, Xiangyu (2026). ProEchoMem: enhancing long video understanding via multi-trace probe-echo memory. SIGIR '26: The 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, VIC, Australia, 20-24 July 2026. New York, NY, United States: ACM. doi: 10.1145/3805712.3809599 |
|
2026 Conference Publication ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender SystemsZhang, Yi, Zhang, Yiwen, Zheng, Kai, Chen, Tong and Yin, Hongzhi (2026). ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems. New York, NY, USA: ACM. doi: 10.1145/3805712.3809600 |
|
2026 Journal Article Multi-agents based user values mining for recommendationChen, Lijian, Yuan, Wei, Chen, Tong, Zhao, Xiangyu, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2026). Multi-agents based user values mining for recommendation. Data Science and Engineering, 1-20. doi: 10.1007/s41019-026-00349-7 |
|
2026 Journal Article H Mamba: hyperbolic mamba for sequential recommendationZhang, Qianru, Wen, Honggang, Yuan, Wei, Chen, Crystal, Yang, Menglin, Yiu, Siu-Ming and Yin, Hongzhi (2026). H Mamba: hyperbolic mamba for sequential recommendation. ACM Transactions on Information Systems, 44 (5) 3811405, 1-27. doi: 10.1145/3811405 |
|
2026 Journal Article Cassette: case-to-case structural distillation for efficient legal case retrievalTang, Yanran, Qiu, Ruihong, Yin, Hongzhi, Li, Xue and Huang, Zi (2026). Cassette: case-to-case structural distillation for efficient legal case retrieval. ACM Transactions on Information Systems, 44 (5) 3816732, 1-27. doi: 10.1145/3816732 |
|
2026 Journal Article A systematic survey and benchmark of deep learning for molecular property prediction in the foundation model eraLi, Zongru, Chen, Xingsheng, Wen, Honggang, Zhang, Regina Qianru, Li, Ming, Zhang, Xiaojin, Yin, Hongzhi, Yang, Qiang, Lam, Kwok-Yan, Lio, Pietro and Yiu, Siu-Ming (2026). A systematic survey and benchmark of deep learning for molecular property prediction in the foundation model era. Journal of Chemical Theory and Computation, 22 (10) acs.jctc.5c02081, 4866-4887. doi: 10.1021/acs.jctc.5c02081 |
|
2026 Conference Publication An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled DataPham, Trinh, Nguyen, Thanh Tam, Huynh, Viet, Yin, Hongzhi and Nguyen, Quoc Viet Hung (2026). An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data. IEEE. doi: 10.1109/icde65706.2026.00182 |
|
2026 Conference Publication Boosting small language models for Text-to-SQL with fine-grained execution feedback and cost-efficient rewardsHoang, Thanh Dat, Huynh, Thanh Trung, Weidlich, Matthias, Nguyen, Thanh Tam, Chen, Tong, Yin, Hongzhi and Nguyen, Quoc Viet Hung (2026). Boosting small language models for Text-to-SQL with fine-grained execution feedback and cost-efficient rewards. 2026 IEEE 42nd International Conference on Data Engineering (ICDE), Montreal, QC, Canada, 4-8 May 2026. Piscataway, NJ, United States: IEEE. doi: 10.1109/icde65706.2026.00183 |
|
2026 Conference Publication ProEx: a unified framework leveraging large language model with profile extrapolation for recommendationZhang, Yi, Zhang, Yiwen, Wang, Yu, Chen, Tong and Yin, Hongzhi (2026). ProEx: a unified framework leveraging large language model with profile extrapolation for recommendation. KDD '26: Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Jeju, Korea, 9 - 13 August 2026. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3770854.3780284 |
|
2026 Conference Publication Efficient content-based recommendation model training via noise-aware coreset selectionTran, Hung Vinh, Chen, Tong, Wen, Hechuan, Nguyen, Quoc Viet Hung, Cui, Bin and Yin, Hongzhi (2026). Efficient content-based recommendation model training via noise-aware coreset selection. WWW '26: Proceedings of the ACM Web Conference 2026, Dubai United Arab Emirates, 13 - 17 April 2026. New York, NY, United States: Association for Computing Machinery, Inc. doi: 10.1145/3774904.3792686 |
|
2026 Conference Publication Relational database distillation: from structured tables to condensed graph dataGao, Xinyi, Zhang, Jingxi, Chen, Lijian, Chen, Tong, Cui, Lizhen and Yin, Hongzhi (2026). Relational database distillation: from structured tables to condensed graph data. WWW '26: The ACM Web Conference 2026, Dubai, United Arab Emirates, 13 - 17 April 2026. New York, NY, United States: ACM. doi: 10.1145/3774904.3792734 |
|
2026 Journal Article Tackling data heterogeneity in federated time series forecastingYuan, Wei, Yang, Chaoqun, Zhao, Xiangyu, Nguyen, Quoc Viet Hung, Cao, Yang, He, Tieke and Yin, Hongzhi (2026). Tackling data heterogeneity in federated time series forecasting. Science China-Information Sciences, 69 (5) 152102. doi: 10.1007/s11432-025-4553-x |
