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Sparse Enhanced Network: An Adversarial Generation Method for Robust Augmentation in Sequential Recommendation Conference paper
Chen, Junyang, Zou, Guoxuan, Zhou, Pan, Yirui, Wu, Chen, Zhenghan, Su, Houcheng, Wang, Huan, Gong, Zhiguo. Sparse Enhanced Network: An Adversarial Generation Method for Robust Augmentation in Sequential Recommendation[C], 2024, 8283-8291.
Authors:  Chen, Junyang;  Zou, Guoxuan;  Zhou, Pan;  Yirui, Wu;  Chen, Zhenghan; et al.
Favorite | TC[WOS]:1 TC[Scopus]:1 | Submit date：2024/05/16
Dmkm: Graph Mining, Social Network Analysis & Community  Dmkm: Anomaly/outlier Detection  Dmkm: Recommender Systems  Ml: Deep Learning Algorithms  Ml: Deep Learning Theory  Ml: Graph-based Machine Learning  Ml: Semi-supervised Learning  Ml: Transparent, Interpretable, Explainable Ml  Ml: Unsupervised & Self-supervised Learning  
Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation Conference paper
Zhang, Zhaofan, Xiao, Yanan, Jiang, Lu, Yang, Dingqi, Yin, Minghao, Wang, Pengyang. Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation[C], 2024, 9396-9404.
Authors:  Zhang, Zhaofan;  Xiao, Yanan;  Jiang, Lu;  Yang, Dingqi;  Yin, Minghao; et al.
Favorite | TC[WOS]:1 TC[Scopus]:5 | Submit date：2024/05/16
Dmkm: Mining Of Spatial  TempOral Or spatio-TempOral Data  Dmkm: Recommender Systems  
Meta-path Based Neighbors for Behavioral Target Generalization in Sequential Recommendation Journal article
Chen, Junyang, Gong, Zhiguo, Li, Yuanman, Zhang, Huanjian, Yu, Hongyong, Zhu, Junzhang, Fan, Ge, Wu, Xiao Ming, Wu, Kaishun. Meta-path Based Neighbors for Behavioral Target Generalization in Sequential Recommendation[J]. IEEE Transactions on Network Science and Engineering, 2022, 9(3), 1658-1667.
Authors:  Chen, Junyang;  Gong, Zhiguo;  Li, Yuanman;  Zhang, Huanjian;  Yu, Hongyong; et al.
Favorite | TC[WOS]:15 TC[Scopus]:19  IF:6.7/6.0 | Submit date：2022/05/17
Behavioral Target Generalization  Sequential Recommendation  Ctr Prediction  Recommender Systems  
Field-aware Variational Autoencoders for Billion-scale User Representation Learning Conference paper
Ge Fan, Chaoyun Zhang, Junyang Chen, Baopu Li, Zenglin Xu, Yingjie Li, Luyu Peng, Zhiguo Gong. Field-aware Variational Autoencoders for Billion-scale User Representation Learning[C], 2022, 3413-3425.
Authors:  Ge Fan;  Chaoyun Zhang;  Junyang Chen;  Baopu Li;  Zenglin Xu; et al.
Favorite | TC[WOS]:5 TC[Scopus]:7 | Submit date：2022/08/29
Lookalike Systems  Recommender Systems  User Representation Learning  Variational Autoencoder  
Improving Conversational Recommender System by Pretraining Billion-scale Knowledge Graph Conference paper
Chi-Man Wong, Fan Feng, Wen Zhang, Chi-Man Vong, Hui Chen, Yichi Zhang, Peng He, Huan Chen, Kun Zhao, Huajun Chen. Improving Conversational Recommender System by Pretraining Billion-scale Knowledge Graph[C]:IEEE, 2021, 2607-2612.
Authors:  Chi-Man Wong;  Fan Feng;  Wen Zhang;  Chi-Man Vong;  Hui Chen; et al.
Favorite | TC[WOS]:19 TC[Scopus]:32 | Submit date：2022/08/09
Conversational Recommender Systems (Crs)  Click-through Rate (Ctr)  K-dcn  
A common topic transfer learning model for crossing city POI recommendations Journal article
Dichao Li, Zhiguo Gong, Defu Zhang. A common topic transfer learning model for crossing city POI recommendations[J]. IEEE Transactions on Cybernetics, 2019, 49(12), 4282-4295.
Authors:  Dichao Li;  Zhiguo Gong;  Defu Zhang
Favorite | TC[WOS]:23 TC[Scopus]:24  IF:9.4/10.3 | Submit date：2021/03/09
Graphical Models  Machine Learning  Recommender Systems  Transfer Learning  
Towards Personalized Learning Through Class Contextual Factors-Based Exercise Recommendation Conference paper
Huo, Yujia, Xiao, Jiang, Ni, Lionel M.. Towards Personalized Learning Through Class Contextual Factors-Based Exercise Recommendation[C], 2019, 85-92.
Authors:  Huo, Yujia;  Xiao, Jiang;  Ni, Lionel M.
Favorite | TC[WOS]:9 TC[Scopus]:12 | Submit date：2022/04/15
Attribute-based Recommendation  Learning Remediation  Performance Prediction  Personalized Learning  Q-matrix  Recommender Systems