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2026

Journal Article

A survey of generative techniques for spatial-temporal data mining

Zhang, Qianru, Wang, Haixin, Wen, Honggang, Long, Cheng, Su, Liangcai, He, Xingwei, Wu, Tailin, Jensen, Christian S., Yiu, Siu-Ming and Yin, Hongzhi (2026). A survey of generative techniques for spatial-temporal data mining. Data Science and Engineering. doi: 10.1007/s41019-026-00346-w

A survey of generative techniques for spatial-temporal data mining

2026

Conference Publication

SmartAgent: chain-of-user-thought for embodied personalized agent in cyber world

Zhang, Jiaqi, Gao, Chen, Zhang, Liyuan, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2026). SmartAgent: chain-of-user-thought for embodied personalized agent in cyber world. Fortieth AAAI Conference on Artificial Intelligence, Singapore, Singapore, 20 - 27 January 2026. Washington, DC, United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v40i21.38859

SmartAgent: chain-of-user-thought for embodied personalized agent in cyber world

2026

Journal Article

A comprehensive survey on imbalanced data learning

Gao, Xinyi, Xie, Dongting, Zhang, Yihang, Wang, Zhengren, Chen, Chong, He, Conghui, Yin, Hongzhi and Zhang, Wentao (2026). A comprehensive survey on imbalanced data learning. Frontiers of Computer Science, 20 (11) 2011622. doi: 10.1007/s11704-025-50274-7

A comprehensive survey on imbalanced data learning

2026

Journal Article

A survey on sequential recommendation

Pan, Li-Wei, Pan, Wei-Ke, Wei, Mei-Yan, Yin, Hong-Zhi and Ming, Zhong (2026). A survey on sequential recommendation. Frontiers of Computer Science, 20 (3) 2003606. doi: 10.1007/s11704-025-41329-w

A survey on sequential recommendation

2026

Conference Publication

On-device large language models for sequential recommendation

Xia, Xin, Yin, Hongzhi and Culpepper, Shane (2026). On-device large language models for sequential recommendation. WSDM '26: Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining, Boise, ID, United States, 22-26 February 2026. New York, NY, United States: ACM. doi: 10.1145/3773966.3777961

On-device large language models for sequential recommendation

2026

Journal Article

Generating Compressed Counterfactual Hard Negative Samples for Graph Contrastive Learning

Yang, Haoran, Chen, Hongxu, Zhao, Xiangyu, Zhang, Sixiao, Sun, Xiangguo, Li, Qian, Yin, Hongzhi and Xu, Guandong (2026). Generating Compressed Counterfactual Hard Negative Samples for Graph Contrastive Learning. CAAI Transactions on Intelligence Technology cit2.70102, 1-14. doi: 10.1049/cit2.70102

Generating Compressed Counterfactual Hard Negative Samples for Graph Contrastive Learning

2026

Journal Article

PPA plus plus : Preference Prototype-Aware Learning with Large Language Model for Universal Cross-Domain Recommendation

Zhang, Yuxi, Zhang, Ji, Xu, Feiyang, Chen, Lvying, Li, Bohan, Wang, Ning, Tu, Huawei, Guo, Lei and Yin, Hongzhi (2026). PPA plus plus : Preference Prototype-Aware Learning with Large Language Model for Universal Cross-Domain Recommendation. Data Science and Engineering, 11 (1), 213-229. doi: 10.1007/s41019-025-00322-w

PPA plus plus : Preference Prototype-Aware Learning with Large Language Model for Universal Cross-Domain Recommendation

2026

Journal Article

Sparse gradient training for recommender systems

Qu, Yunke, Qu, Liang, Chen, Tong, Zhao, Xiangyu, Li, Jianxin and Yin, Hongzhi (2026). Sparse gradient training for recommender systems. Data Science and Engineering, 11 (1), 230-247. doi: 10.1007/s41019-025-00327-5

Sparse gradient training for recommender systems

2026

Journal Article

DeepCGC: unveiling the deep clustering mechanism of fast graph condensation

Gao, Xinyi, Li, Wenjie, Zhao, Xiangyu, Chen, Tong, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2026). DeepCGC: unveiling the deep clustering mechanism of fast graph condensation. IEEE Transactions on Knowledge and Data Engineering, 38 (3), 1575-1588. doi: 10.1109/tkde.2026.3655841

DeepCGC: unveiling the deep clustering mechanism of fast graph condensation

2026

Journal Article

Towards on-device personalization: cloud-device collaborative data augmentation for efficient on-device language model

Zhong, Zhaofeng, Yuan, Wei, Qu, Liang, Chen, Tong, Wang, Hao, Zhao, Xiangyu and Yin, Hongzhi (2026). Towards on-device personalization: cloud-device collaborative data augmentation for efficient on-device language model. ACM Transactions on Intelligent Systems and Technology, 17 (1) 3779452, 1-1. doi: 10.1145/3779452

Towards on-device personalization: cloud-device collaborative data augmentation for efficient on-device language model

2026

Journal Article

Erratum: Lightweight Embeddings with Graph Rewiring for Collaborative Filtering

Liang, Xurong, Chen, Tong, Yuan, Wei and Yin, Hongzhi (2026). Erratum: Lightweight Embeddings with Graph Rewiring for Collaborative Filtering. ACM Transactions on Information Systems, 44 (1) C1, 1-1. doi: 10.1145/3785705

Erratum: Lightweight Embeddings with Graph Rewiring for Collaborative Filtering

2026

Journal Article

Scalable and effective negative sample generation for hyperedge prediction

Qu, Shilin, Wang, Weiqing, Li, Yuan-Fang, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2026). Scalable and effective negative sample generation for hyperedge prediction. Neural Networks, 193 108034, 1-13. doi: 10.1016/j.neunet.2025.108034

Scalable and effective negative sample generation for hyperedge prediction

2026

Conference Publication

Memory-enhanced invariant prompt learning for urban flow prediction under distribution shifts

Jiang, Haiyang, Chen, Tong, Zhang, Wentao, Nguyen, Quoc Viet Hung, Yuan, Yuan, Li, Yong and Yin, Hongzhi (2026). Memory-enhanced invariant prompt learning for urban flow prediction under distribution shifts. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025, Porto, Portugal, 15-19 September 2025. Cham, Switzerland: Springer. doi: 10.1007/978-3-032-06066-2_9

Memory-enhanced invariant prompt learning for urban flow prediction under distribution shifts

2026

Journal Article

ARLIE: Adaptive Reinforcement Learning with Inductive Embeddings for Fully-inductive Multi-hop Reasoning over Temporal Knowledge Graphs

Zheng, Shangfei, Gao, Yunjun, Liu, An, Li, Wenhao, Chen, Tong and Yin, Hongzhi (2026). ARLIE: Adaptive Reinforcement Learning with Inductive Embeddings for Fully-inductive Multi-hop Reasoning over Temporal Knowledge Graphs. IEEE Transactions on Knowledge and Data Engineering, 38 (5), 1-14. doi: 10.1109/tkde.2026.3666242

ARLIE: Adaptive Reinforcement Learning with Inductive Embeddings for Fully-inductive Multi-hop Reasoning over Temporal Knowledge Graphs

2026

Journal Article

M2Rec: Multi-scale Mamba for efficient sequential Recommendation

Zhang, Qianru, Qu, Liang, Wen, Honggang, Huang, Dong, Yiu, Siu-Ming, Hung, Nguyen Quoc Viet and Yin, Hongzhi (2026). M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation. IEEE Transactions on Knowledge and Data Engineering, PP (99), 1-14. doi: 10.1109/TKDE.2026.3700323

M2Rec: Multi-scale Mamba for efficient sequential Recommendation

2026

Journal Article

Self-Gating Attention for Efficient Time Series Forecasting

Wang, Dezheng, Chen, Tong, Yuan, Wei, Chen, Congyan, Li, Shihua and Yin, Hongzhi (2026). Self-Gating Attention for Efficient Time Series Forecasting. IEEE Transactions on Industrial Informatics, PP (99), 1-12. doi: 10.1109/tii.2026.3710167

Self-Gating Attention for Efficient Time Series Forecasting

2026

Conference Publication

LogicGate: adaptive rule-based modeling of exogenous effects for time series forecasting

Zhong, Jie, Chen, Tong, Yuan, Wei, Cui, Lizhen and Yin, Hongzhi (2026). LogicGate: Adaptive Rule-Based Modeling of Exogenous Effects for Time Series Forecasting. Lecture Notes in Computer Science. (pp. 239-255) Singapore: Springer Nature Singapore. doi: 10.1007/978-981-92-0375-8_15

LogicGate: adaptive rule-based modeling of exogenous effects for time series forecasting

2026

Journal Article

A review of instruction-guided image editing

Nguyen, Thanh Tam, Ren, Zhao, Pham, Trinh, Nguyen, Phi Le, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2026). A review of instruction-guided image editing. Engineering Applications of Artificial Intelligence, 163 (Part 2) 112953, 1-35. doi: 10.1016/j.engappai.2025.112953

A review of instruction-guided image editing

2025

Journal Article

ZhiFangDanTai: fine-tuning graph-based retrieval-augmented generation model for traditional Chinese medicine formula

Zhang, Zixuan, Hao, Bowen, Li, Yingjie and Yin, Hongzhi (2025). ZhiFangDanTai: fine-tuning graph-based retrieval-augmented generation model for traditional Chinese medicine formula. IEEE Journal of Biomedical and Health Informatics, 30 (4), 3118-3131. doi: 10.1109/jbhi.2025.3607819

ZhiFangDanTai: fine-tuning graph-based retrieval-augmented generation model for traditional Chinese medicine formula

2025

Journal Article

When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation

Wang, Zongwei, Gao, Min, Yu, Junliang, Sadiq, Shazia, Yin, Hongzhi and Liu, Ling (2025). When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation. ACM Transactions on Information Systems, 44 (2) 3779448, 1-30. doi: 10.1145/3779448

When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation