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2026 Journal Article Handling data sparsity and model poisoning attacks in federated sequential recommender systemsNguyen, 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 |
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2026 Conference Publication On-device large language models for sequential recommendationXia, 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 |
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2026 Journal Article Generating Compressed Counterfactual Hard Negative Samples for Graph Contrastive LearningYang, 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 |
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2026 Journal Article PPA plus plus : Preference Prototype-Aware Learning with Large Language Model for Universal Cross-Domain RecommendationZhang, 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. doi: 10.1007/s41019-025-00322-w |
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2026 Journal Article Sparse gradient training for recommender systemsQu, Yunke, Qu, Liang, Chen, Tong, Zhao, Xiangyu, Li, Jianxin and Yin, Hongzhi (2026). Sparse gradient training for recommender systems. Data Science and Engineering, 1-18. doi: 10.1007/s41019-025-00327-5 |
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2026 Journal Article Towards on-device personalization: cloud-device collaborative data augmentation for efficient on-device language modelZhong, 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 |
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2026 Journal Article Erratum: Lightweight Embeddings with Graph Rewiring for Collaborative FilteringLiang, 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 |
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2026 Journal Article DeepCGC: Unveiling the Deep Clustering Mechanism of Fast Graph CondensationGao, 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 |
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2026 Journal Article A review of instruction-guided image editingNguyen, 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 |
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2026 Journal Article Scalable and effective negative sample generation for hyperedge predictionQu, 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 |
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2026 Conference Publication Memory-enhanced invariant prompt learning for urban flow prediction under distribution shiftsJiang, 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 |
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2026 Journal Article ARLIE: Adaptive Reinforcement Learning with Inductive Embeddings for Fully-inductive Multi-hop Reasoning over Temporal Knowledge GraphsZheng, 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, 1-14. doi: 10.1109/tkde.2026.3666242 |
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2025 Journal Article When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in RecommendationWang, 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 |
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2025 Journal Article A data-driven scale-adaptive time-frequency convolutional network for long sequence time-series forecastingZhang, 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 |
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2025 Journal Article DecKG: decentralized collaborative learning with knowledge graph enhancement for POI recommendationZheng, 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 |
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2025 Journal Article On-device recommender systems: a comprehensive surveyYin, 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 |
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2025 Conference Publication NR-GCF: Graph Collaborative Filtering with Improved Noise ResistanceChen, Yijun, Li, Bohan, Li, Yicong, Song, Lixiang, Wang, Haofen, Wu, Wenlong, Zhuo, Junnan and Yin, Hongzhi (2025). NR-GCF: Graph Collaborative Filtering with Improved Noise Resistance. 34th ACM International Conference on Information and Knowledge Management CIKM 2025, Seoul, Korea, 10 - 14 November 2025. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3746252.3761342 |
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2025 Conference Publication HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal LearningZhang, Qianru, Gao, Xinyi, Wang, Haixin, Huang, Dong, Yiu, Siu-Ming and Yin, Hongzhi (2025). HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning. 34th ACM International Conference on Information and Knowledge Management CIKM 2025, Seoul, Korea, 10 - 14 November 2025. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3746252.3761383 |
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2025 Conference Publication Efficient multimodal streaming recommendation via Expandable Side Mixture-of-ExpertsQu, Yunke, Qu, Liang, Chen, Tong, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2025). Efficient multimodal streaming recommendation via Expandable Side Mixture-of-Experts. CIKM '25: The 34th ACM International Conference on Information and Knowledge Management, Seoul, South Korea, 10-14 November 2025. New York, United States: Association for Computing Machinery. doi: 10.1145/3746252.3761390 |
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2025 Conference Publication Harnessing large language models for Group POI recommendationsLong, Jing, Qu, Liang, Yu, Junliang, Chen, Tong, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2025). Harnessing large language models for Group POI recommendations. 34th ACM International Conference on Information and Knowledge Management CIKM 2025, Seoul, Republic of Korea, 10-14 November 2025. New York, NY, United States: ACM. doi: 10.1145/3746252.3761018 |
