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2026

Journal Article

H <scp>Mamba</scp> : Hyperbolic Mamba for Sequential Recommendation

Zhang, 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 3811405. doi: 10.1145/3811405

H <scp>Mamba</scp> : Hyperbolic Mamba for Sequential Recommendation

2026

Conference Publication

Relational Database Distillation: From Structured Tables to Condensed Graph Data

Gao, Xinyi, Zhang, Jingxi, Chen, Lijian, Chen, Tong, Cui, Lizhen and Yin, Hongzhi (2026). Relational Database Distillation: From Structured Tables to Condensed Graph Data. New York, NY, USA: ACM. doi: 10.1145/3774904.3792734

Relational Database Distillation: From Structured Tables to Condensed Graph Data

2026

Journal Article

Handling data sparsity and model poisoning attacks in federated sequential recommender systems

Nguyen, Minh Hieu, Nguyen, Thanh Tam, Jo, Jun, Nguyen, Duc Anh, Yin, Hongzhi and Nguyen, Quoc Viet Hung (2026). Handling data sparsity and model poisoning attacks in federated sequential recommender systems. Knowledge-Based Systems, 338 115545, 1-12. doi: 10.1016/j.knosys.2026.115545

Handling data sparsity and model poisoning attacks in federated sequential recommender systems

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, 1-25. doi: 10.1007/s41019-026-00346-w

A Survey of Generative Techniques for Spatial-Temporal Data Mining

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

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

DeepCGC: Unveiling the Deep Clustering Mechanism of Fast Graph Condensation

Gao, Xinyi, Li, Wenjie, Chen, Tong, Zhao, Xiangyu, 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, PP (99), 1-14. doi: 10.1109/tkde.2026.3655841

DeepCGC: Unveiling the Deep Clustering Mechanism of Fast Graph Condensation

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 112953, 112953. 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

2025

Journal Article

A data-driven scale-adaptive time-frequency convolutional network for long sequence time-series forecasting

Zhang, Zhiqiang, Wang, Weiqing, Zhou, Xin, Bai, Yu and Yin, Hongzhi (2025). A data-driven scale-adaptive time-frequency convolutional network for long sequence time-series forecasting. IEEE Transactions on Knowledge and Data Engineering, 37 (12), 6750-6764. doi: 10.1109/TKDE.2025.3619521

A data-driven scale-adaptive time-frequency convolutional network for long sequence time-series forecasting

2025

Journal Article

DecKG: decentralized collaborative learning with knowledge graph enhancement for POI recommendation

Zheng, Ruiqi, Qu, Liang, Ye, Guanhua, Chen, Tong, Shi, Yuhui and Yin, Hongzhi (2025). DecKG: decentralized collaborative learning with knowledge graph enhancement for POI recommendation. Information Sciences, 721 122570, 122570-721. doi: 10.1016/j.ins.2025.122570

DecKG: decentralized collaborative learning with knowledge graph enhancement for POI recommendation

2025

Journal Article

On-device recommender systems: a comprehensive survey

Yin, Hongzhi, Qu, Liang, Chen, Tong, Yuan, Wei, Zheng, Ruiqi, Long, Jing, Xia, Xin, Shi, Yuhui and Zhang, Chengqi (2025). On-device recommender systems: a comprehensive survey. Data Science and Engineering, 10 (4), 591-620. doi: 10.1007/s41019-025-00308-8

On-device recommender systems: a comprehensive survey