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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 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, 38 (5), 1-14. doi: 10.1109/tkde.2026.3666242 |
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2026 Journal Article M2Rec: Multi-scale Mamba for efficient sequential RecommendationZhang, 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 |
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2026 Book Chapter LogicGate: Adaptive Rule-Based Modeling of Exogenous Effects for Time Series ForecastingZhong, 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 |
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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 (Part 2) 112953, 1-35. doi: 10.1016/j.engappai.2025.112953 |
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2025 Journal Article ZhiFangDanTai: fine-tuning graph-based retrieval-augmented generation model for traditional Chinese medicine formulaZhang, 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 |
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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 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 |
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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, NY United States: Association for Computing Machinery. doi: 10.1145/3746252.3761390 |
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2025 Journal Article Teaching MLPs to master heterogeneous graph-structured knowledge for efficient and accurate inferenceLiu, Yunhui, Gao, Xinyi, He, Tieke, Zhao, Jianhua and Yin, Hongzhi (2025). Teaching MLPs to master heterogeneous graph-structured knowledge for efficient and accurate inference. IEEE Transactions on Knowledge and Data Engineering (10), 6189-6201. doi: 10.1109/tkde.2025.3589596 |
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2025 Journal Article Enhancing language models with commonsense knowledge for multi-turn response selectionWang, Yuandong, Ren, Xuhui, Chen, Tong, Yin, Hongzhi and Hung, Nguyen Quoc Viet (2025). Enhancing language models with commonsense knowledge for multi-turn response selection. International Journal of Machine Learning and Cybernetics, 16 (12), 10421-10441. doi: 10.1007/s13042-025-02804-9 |
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2025 Journal Article FELLAS: enhancing federated sequential recommendation with LLM as external servicesYuan, Wei, Yang, Chaoqun, Ye, Guanhua, Chen, Tong, Hung, Nguyen Quoc Viet and Yin, Hongzhi (2025). FELLAS: enhancing federated sequential recommendation with LLM as external services. ACM Transactions on Information Systems, 43 (6) 144, 1-24. doi: 10.1145/3709138 |
