Publications [Google Scholar] [DBLP]

arXiv Preprints

  1. Aligning Crowd Feedback via Distributional Preference Reward Modeling
    Dexun Li, Cong Zhang, Kuicai Dong, Derrick Goh Xin Deik, Ruiming Tang, Yong Liu
    arXiv:2402.09764, May 2024

  2. Unsupervised Representation Learning for Time Series: A Review
    Qianwen Meng, Hangwei Qian, Yong Liu, Yonghui Xu, Zhiqi Shen, Lizhen Cui
    arXiv:2308.01578, Aug. 2023
    [Code & Data]

Selected Conference & Journal Papers

2024

  1. [KDD’24] Dataset Regeneration for Sequential Recommendation
    Mingjia Yin, Hao Wang, Wei Guo, Yong Liu, Suojuan Zhang, Sirui Zhao, Defu Lian, Enhong Chen
    Proceedings of the 30th SIGKDD Conference on Knowledge Discovery and Data Mining, Accepted, Aug. 25-29, 2024

  2. [WWW’24] Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
    Yongqiang Han, Hao Wang, Kefan Wang, Likang Wu, Zhi Li, Wei Guo, Yong Liu, Defu Lian, Enhong Chen
    Proceedings of the ACM Web Conference 2024, Pages 3297-3306, Accepted, May 13-17, 2024

  3. [WSDM’24] User Behavior Enriched Temporal Knowledge Graph for Sequential Recommendation
    Hengchang Hu, Wei Guo, Xu Liu, Yong Liu, Ruiming Tang, Rui Zhang, Min-Yen Kan
    Proceedings of the 17th ACM International Conference on Web Search and Data Mining. Pages 266-275. Mar. 4-8, 2024

  4. [ESA] Estimating Package Arrival Time via Heterogeneous Hypergraph Neural Network
    Lei Zhang, Xingyu Wu, Yong Liu, Xin Zhou, Yiming Cao, Yonghui Xu, Lizhen Cui, Chunyan Miao
    Expert Systems with Applications, Accepted, Mar. 2024

  5. [TNNLS] A Survey on Federated Recommendation Systems
    Zehua Sun, Yonghui Xu, Yong Liu, Wei He, Yali Jiang, Fangzhao Wu, Lizhen Cui
    IEEE Transactions on Neural Networks and Learning Systems, Accepted, Feb. 2024

  6. [TKDD] Package Arrival Time Prediction via Knowledge Distillation Graph Neural Network
    Lei Zhang, Yong Liu, Zhiwei Zeng, Yiming Cao, Xingyu Wu, Yonghui Xu, Zhiqi Shen Shen, Lizhen Cui
    ACM Transactions on Knowledge Discovery from Data, Accepted, Feb. 2024

  7. [TOIS] Collaborative Sequential Recommendations via Multi-View GNN-Transformers
    Tianze Luo, Yong Liu, Sinno Pan
    ACM Transactions on Information Systems, Accepted, Jan. 2024

2023

  1. [CIKM’23] Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems
    Hengchang Hu, Wei Guo, Yong Liu, Min-Yen Kan
    Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Accepted, Oct. 21-25, 2023

  2. [CIKM’23] DFFM: Domain Facilitated Feature Modeling for CTR Prediction
    Wei Guo, Chenxu Zhu, Fan Yan, Bo Chen, Weiwen Liu, Huifeng Guo, Hongkun Zheng, Yong Liu, Ruiming Tang
    Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Accepted, Oct. 21-25, 2023

  3. [CIKM’23] APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
    Mingjia Yin, Hao Wang, Xiang Xu, Likang Wu, Sirui Zhao, Wei Guo, Yong Liu, Ruiming Tang, Defu Lian, Enhong Chen
    Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Accepted, Oct. 21-25, 2023

  4. [Bioinformatics] KR4SL: Knowledge Graph Reasoning for Explainable Prediction of Synthetic Lethality
    Ke Zhang, Min Wu, Yong Liu, Yimiao Feng, Jie Zheng
    Bioinformatics, Volume 39, Issue Supplement_1, Pages i158–i167, Jun. 2023
    [Code & Data]

  5. [TNNLS] A Survey on Reinforcement Learning for Recommender Systems
    Yuanguo Lin, Yong Liu, Fan Lin, Lixin Zou, Pengcheng Wu, Wenhua Zeng, Huanhuan Chen, Chunyan Miao
    IEEE Transactions on Neural Networks and Learning Systems, Accepted, May 2023

  6. [TNNLS] Multi-Component Adversarial Domain Adaptation: A General Framework
    Changan Yi, Haotian Chen, Yonghui Xu, Huanhuan Chen, Yong Liu, Haishu Tan, Yugang Yan, Han Yu
    IEEE Transactions on Neural Networks and Learning Systems, Accepted, Apr. 2023

  7. [WWW’23] Bootstrap Latent Representations for Multi-modal Recommendation
    Xin Zhou, Hongyu Zhou, Yong Liu, Zhiwei Zeng, Chunyan Miao, Pengwei Wang, Yuan You, Feijun Jiang
    Proceedings of the ACM Web Conference 2023. Pages 845-854, Apr. 30 - May 4, 2023
    [Code & Data]

  8. [ICDE’23] Layer-refined Graph Convolutional Networks for Recommendation
    Xin Zhou, Donghui Lin, Yong Liu, Chunyan Miao
    Proceedings of the 39th IEEE International Conference on Data Engineering, Accepted, Apr. 3-7, 2023
    [Code & Data]

  9. [ICDE’23] Cross-Domain Disentangled Learning for E-Commerce Live Streaming Recommendation
    Yixin Zhang, Yong Liu, Hao Xiong, Yi Liu, Fuqiang Yu, Wei He, Yonghui Xu, Lizhen Cui, Chunyan Miao
    Proceedings of the 39th IEEE International Conference on Data Engineering, Accepted, Apr. 3-7, 2023

  10. [ICDE’23] Delivery Time Prediction Using Large-Scale Graph Structure Learning Based on Quantile Regression
    Lei Zhang, Xin Zhou, Yiming Cao, Yonghui Xu, Mingliang Wang, Xingyu Wu, Yong Liu, Lizhen Cui, Zhiqi Shen
    Proceedings of the 39th IEEE International Conference on Data Engineering, Accepted, Apr. 3-7, 2023

  11. [TORS] SELFCF: A Simple Framework for Self-supervised Collaborative Filtering
    Xin Zhou, Aixin Sun, Yong Liu, Jie Zhang, Chunyan Miao
    ACM Transactions on Recommender Systems, Accepted, Mar. 2023
    [Code & Data]

  12. [WSDM’23] Inductive Graph Transformer for Delivery Time Estimation
    Xin Zhou, Jinglong Wang, Yong Liu, Xingyu Wu, Zhiqi Shen, Cyril Leung
    Proceedings of the 16th ACM International Conference on Web Search and Data Mining. Pages 679-687. Feb. 27-Mar. 3, 2023

  13. [AAAI’23] Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective
    Yongwei Wang, Yong Liu, Zhiqi Shen
    Proceedings of the 37th AAAI Conference on Artificial Intelligence, Accepted, Feb. 7-14, 2023

  14. [AAAI’23] Next POI Recommendation with Dynamic Graph and Explicit Dependency
    Feiyu Yin, Yong Liu, Zhiqi Shen, Lisi Chen, Shuo Shang, Peng Han
    Proceedings of the 37th AAAI Conference on Artificial Intelligence, Accepted, Feb. 7-14, 2023

  15. [AAAI’23] MHCCL: Masked Hierarchical Cluster-wise Contrastive Learning for Multivariate Time Series
    Qianwen Meng, Hangwei Qian, Yong Liu, Lizhen Cui, Yonghui Xu, Zhiqi Shen
    Proceedings of the 37th AAAI Conference on Artificial Intelligence, Accepted, Feb. 7-14, 2023
    [Code & Data]

  16. [TETCI] Dynamic Multi-objective Optimization Framework with Interactive Evolution for Sequential Recommendation
    Wei Zhou, Yong Liu, Min Li, Yu Wang, Zhiqi Shen, Liang Feng, Zexuan Zhu
    IEEE Transactions on Emerging Topics in Computational Intelligence, Accepted, Feb. 2023

2022

  1. [EMNLP’22] History-aware Hierarchical Transformer for Multi-session Open-domain Dialogue System
    Tong Zhang, Yong Liu, Boyang Li, Zhiwei Zeng, Pengwei Wang, Yuan You, Chunyan Miao, Lizhen Cui
    Findings of the Association for Computational Linguistics: EMNLP 2022. Pages 3395-3407. Dec. 7-11, 2022

  2. [TKDE] Aspect-guided Syntax Graph Learning for Explainable Recommendation
    Yidan Hu, Yong Liu, Chunyan Miao, Gongqi Lin, Yuan Miao
    IEEE Transactions on Knowledge and Data Engineering, Accepted, Oct. 2022

  3. [CIKM’22] Memory Bank Augmented Long-tail Sequential Recommendation
    Yidan Hu, Yong Liu, Chunyan Miao, Yuan Miao
    Proceedings of the 31st ACM International Conference on Information and Knowledge Management. Pages 791-801. Oct. 17-21, 2022
    [Code & Data]

  4. [Bioinformatics] NSF4SL: Negative-sample-free Contrastive Learning for Ranking Synthetic Lethal Partner Genes in Human Cancers
    Shike Wang, Yimiao Feng, Xin Liu, Yong Liu, Min Wu, Jie Zheng
    Bioinformatics, Volume 38, Issue Supplement_2, Pages ii13–ii19, Sep. 2022
    [Code & Data]

  5. [KDD’22] Graph-Flashback Network for Next Location Recommendation
    Xuan Rao, Lisi Chen, Yong Liu, Shuo Shang, Bin Yao, Peng Han
    Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Pages 1463-1471. Aug. 14-18, 2022
    [Code & Data]

  6. [IJCAI’22] Enhancing Sequential Recommendation with Graph Contrastive Learning
    Yixin Zhang, Yong Liu, Yonghui Xu, Hao Xiong, Chenyi Lei, Wei He, Lizhen Cui, Chunyan Miao
    Proceedings of the 31st International Joint Conference on Artificial Intelligence. Pages 2398-2405. Jul. 23-29, 2022
    [Code & Data]

  7. [DASFAA’22] Diffusion-based Graph Contrastive Learning for Recommendation with Implicit Feedback
    Lingzi Zhang, Yong Liu, Xin Zhou, Chunyan Miao, Guoxin Wang, Haihong Tang
    Proceedings of the 27th International Conference on Database Systems for Advanced Applications. Pages 232–247. Apr. 11-14, 2022
    [Code & Data]

  8. [Bioinformatics] Pre-training Graph Neural Networks for Link Prediction in Biomedical Networks
    Yahui Long, Min Wu, Yong Liu, Yuan Fang, Jinmiao Chen, Chee Keong Kwoh, Jiawei Luo, Xiaoli Li
    Bioinformatics, Volume 38, Issue 8, Pages 2254–2262, Apr. 2022
    [Code & Data]

  9. [AEI] Heterogeneous Star Graph Attention Network for Product Attributes Prediction
    Xuejiao Zhao, Yong Liu, Yonghui Xu, Yonghua Yang, Xusheng Luo, Chunyan Miao
    Advanced Engineering Informatics, Volume 51, Jan. 2022
    [Code & Data]

2021

  1. [CIKM’21] Unsupervised Categorical Representation Learning for Package Arrival Time Prediction
    Yang Li, Xingyu Wu, Jinglong Wang, Yong Liu, Xiaoqing Wang, Yuming Deng, Chunyan Miao
    Proceedings of the 30th ACM International Conference on Information and Knowledge Management. Pages 3935-3944. Nov. 1-5, 2021

  2. [CIKM’21] The Skyline of Counterfactual Explanations for Machine Learning Decision Models
    Yongjie Wang, Qinxu Ding, Ke Wang, Yue Liu, Xingyu Wu, Jinglong Wang, Yong Liu, Chunyan Miao
    Proceedings of the 30th ACM International Conference on Information and Knowledge Management. Pages 2030–2039. Nov. 1-5, 2021
    [Code & Data]

  3. [MM’21] Pre-training Graph Transformer with Multimodal Side Information for Recommendation
    Yong Liu, Susen Yang, Chenyi Lei, Guoxin Wang, Haihong Tang, Juyong Zhang, Aixin Sun, Chunyan Miao
    Proceedings of the 29th ACM International Conference on Multimedia (Oral). Pages 2853-2861. Oct. 20-24, 2021
    [Code & Data]

  4. [MM’21] Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training
    Chenyi Lei, Shixian Luo, Yong Liu, Wanggui He, Jiamang Wang, Guoxin Wang, Haihong Tang, Chunyan Miao, Houqiang Li
    Proceedings of the 29th ACM International Conference on Multimedia. Pages 2567-2576. Oct. 20-24, 2021

  5. [Bioinformatics] Graph Contextualized Attention Network for Predicting Synthetic Lethality in Human Cancers
    Yahui Long, Min Wu, Yong Liu, Jie Zheng, Kwoh Chee Kong, Jiawei Luo, Xiaoli-Li
    Bioinformatics, Volume 37, Issue 16, Pages 2432–2440, Aug. 2021
    [Code & Data]

  6. [KDD’21] SEMI: A Sequential Multi-Modal Information Transfer Network for E-Commerce Micro-Video Recommendations
    Chenyi Lei, Yong Liu, Lingzi Zhang, Guoxin Wang, Haihong Tang, Houqiang Li, Chunyan Miao
    Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Pages 3161–3171. Aug. 14-18, 2021

  7. [KDD’21] Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems
    Yinan Zhang, Boyang Li, Yong Liu, Hao Wang, Chunyan Miao
    Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Pages 2263–2273. Aug. 14-18, 2021
    [Code & Data]

  8. [Bioinformatics] KG4SL: Knowledge Graph Neural Network for Synthetic Lethality Prediction in Human Cancers
    Shike Wang, Fan Xu, Yunyang Li, Jie Wang, Ke Zhang, Yong Liu, Min Wu, Jie Zheng
    Bioinformatics, Volume 37, Issue Supplement_1, Pages i418–i425, Jul. 2021
    [Code & Data]

  9. [TKDE] Neighbor-Anchored Adversarial Graph Neural Networks
    Zemin Liu, Yuan Fang, Yong Liu, Vincent W. Zheng
    IEEE Transactions on Knowledge and Data Engineering, Accepted, May 2021
    [Code & Data]

  10. [TKDE] Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph
    Yong Liu, Susen Yang, Yonghui Xu, Chunyan Miao, Min Wu, Juyong Zhang
    IEEE Transactions on Knowledge and Data Engineering, Accepted, May 2021
    [Code & Data]

  11. [TKDE] Learning Hierarchical Review Graph Representation for Recommendation
    Yong Liu, Susen Yang, Yinan Zhang, Chunyan Miao, Zaiqing Nie, Juyong Zhang
    IEEE Transactions on Knowledge and Data Engineering, Accepted, Apr. 2021
    [Code & Data]

  12. [AAAI’21] A Hybrid Bandit Framework for Diversified Recommendation
    Qinxu Ding, Yong Liu, Chunyan Miao, Fei Cheng, Haihong Tang
    Proceedings of the 35th AAAI Conference on Artificial Intelligence. Pages 4036-4044. Feb. 2-9, 2021

  13. [AAAI’21] Keyword-Guided Neural Conversational Model
    Peixiang Zhong, Yong Liu, Hao Wang, Chunyan Miao
    Proceedings of the 35th AAAI Conference on Artificial Intelligence. Pages 14568-14576. Feb. 2-9, 2021
    [Code & Data]

2020 and Before

  1. [IJCAI’20] Learning Personalized Itemset Mapping for Cross-Domain Recommendation
    Yinan Zhang, Yong Liu, Peng Han, Chunyan Miao, Lizhen Cui, Baoli Li, Haihong Tang
    Proceedings of the 29th International Joint Conference on Artificial Intelligence. Pages 2561-2567. Jan. 7-15, 2021
    [Code & Data]

  2. [IJCAI’20] Contextualized Point-of-Interest Recommendation
    Peng Han, Zhongxiao Li, Yong Liu, Peilin Zhao, Jing Li, Hao Wang, Shuo Shang
    Proceedings of the 29th International Joint Conference on Artificial Intelligence. Pages 2484-2490. Jan. 7-15, 2021
    [Code & Data]

  3. [Bioinformatics] Ensembling graph attention networks for humanmicrobe-drug association prediction
    Yahui Long, Min Wu, Yong Liu, Chee Keong Kwoh, Jiawei Luo, Xiao-li Li
    Bioinformatics, Volume 36, Issue Supplement_2, Pages i779–i786, Dec. 2020
    [Code & Data]

  4. [EMNLP’20] Towards Persona-Based Empathetic Conversational Models
    Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu, Chunyan Miao
    Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Pages 6556–6566. Nov. 16-20, 2020
    [Code & Data]

  5. [AAAI’20] Diversified Interactive Recommendation with Implicit Feedback
    Yong Liu, Yingtai Xiao, Qiong Wu, Chunyan Miao, Juyong Zhang, Binqiang Zhao, Haihong Tang
    Proceedings of the 34th AAAI Conference on Artificial Intelligence. Pages 4932-4939. New York, USA, Feb. 7-12, 2020

  6. [IJCAI’19] PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation
    Qiong Wu, Yong Liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, Lu Guan
    Proceedings of 28th International Joint Conference on Artificial Intelligence. Pages 3870-3876. Macao, China, Aug. 10-16, 2019
    [Code & Data]

  7. [KDD’19] GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization
    Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis
    Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Pages 705-713. Alaska, USA, Aug. 4-8, 2019
    [Code & Data]

  8. [IJCAI’18] Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback
    Yong Liu, Lifan Zhao, Guimei Liu, Xinyan Lu, Peng Gao, Xiao-Li Li, Zhihui Jin
    Proceedings of 27th International Joint Conference on Artificial Intelligence. Pages 3463-3469. Stockholm, Sweden, Jul. 13-19, 2018

  9. [TCBB] SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization
    Yong Liu, Min Wu, Chenghao Liu, Xiao-Li Li, Jie Zheng
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, Volume 17, Issue 3, Pages 748–757, Apr. 2019
    [Code & Data]

  10. [SDM’18] Robust Cost-Sensitive Learning for Recommendation with Implicit Feedback
    Peng Yang, Peilin Zhao, Yong Liu, Xin Gao
    Proceedings of 2018 SIAM International Conference on Data Mining. Pages 621-629. San Diego, California, USA, May 3-5, 2018

  11. [IJCAI’17] Learning User Dependencies for Recommendation
    Yong Liu, Peilin Zhao, Xin Liu, Min Wu, Lixin Duan, Xiao-Li Li
    Proceedings of 26th International Joint Conference on Artificial Intelligence. Pages 2379-2385. Melbourne, Australia, Aug. 19-25, 2017

  12. [IJCAI’17] Online Multitask Relative Similarity Learning
    Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao
    Proceedings of 26th International Joint Conference on Artificial Intelligence. Pages 1823-1829. Melbourne, Australia, Aug. 19-25, 2017

  13. [IJCAI’16] Exploring the Context of Locations for Personalized Location Recommendations
    Xin Liu, Yong Liu, Xiao-Li Li
    Proceedings of 25th International Joint Conference on Artificial Intelligence. Pages 1188-1194. New York City, USA, Jul. 9-16, 2016

  14. [PLOS CB] Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction
    Yong Liu, Min Wu, Chunyan Miao, Peilin Zhao, Xiao-Li Li
    PLOS Computational Biology, Volume 12, Issue 2, e1004760, Feb. 2016
    [Code & Data]

  15. [IJCAI’15] A Boosting Algorithm for Item Recommendation with Implicit Feedback
    Yong Liu, Peilin Zhao, Aixin Sun, Chunyan Miao
    Proceedings of 24th International Joint Conference on Artificial Intelligence. Pages 1792-1798. Buenos Aires, Argentina, Jul. 25-31, 2015
    [Code & Data]

  16. [CIKM’14] Exploiting Geographical Neighborhood Characteristics for Location Recommendation
    Yong Liu, Wei Wei, Aixin Sun, Chunyan Miao
    Proceedings of 23rd ACM International Conference on Information and Knowledge Management. Pages 739-748. Shanghai, China, Nov. 3-7, 2014
    [Code & Data]

  17. [SIGIR’14] Your Neighbors Affect Your Ratings: On Geographical Neighborhood Influence to Rating Prediction
    Longke Hu, Aixin Sun, Yong Liu
    Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. Pages 345-354. Gold Coast, Australia, Jul. 6-11, 2014

  18. [CIKM’13] Personalized Point-of-Interest Recommendation by Mining Users’ Preference Transition
    Xin Liu, Yong Liu, Karl Aberer, Chunyan Miao
    Proceedings of 22nd ACM International Conference on Information and Knowledge Management. Pages 733-738. San Francisco, USA, Oct. 27-Nov. 1, 2013

Workshop Papers

  1. [NLP4ConvAI’22] Toward Knowledge-Enriched Conversational Recommendation Systems
    Tong Zhang, Yong Liu, Boyang Li, Peixiang Zhong, Chen Zhang, Hao Wang, Chunyan Miao
    Proceedings of the 4th Workshop on NLP for Conversational AI. Pages 212–217. May 27, 2022
    [Code & Data]

Book Chapters

  1. Classification of Travel Patterns Including Wandering Based on Bi-directional Long Short-Term Memory Networks
    Nhu Khue Vuong, Yong Liu, Syin Chan, Chiew Tong Lau, Zhenghua Chen, Min Wu and Xiaoli Li
    Generalization With Deep Learning: For Improvement On Sensing Capability, Edited by Zhenghua Chen, Min Wu, Xiaoli Li, ISBN: 978-981-121-885-9, World Scientific, April 2021

  2. Matrix Factorization for Drug-Target Interaction Prediction
    Yong Liu, Min Wu, Peilin Zhao, Xiao-Li Li
    High Performance Computing for Big Data: Methodologies and Applications, Edited by Chao Wang, ISBN: 978-1498783996, CRC Press, October 2017