二、代表成果 [1] Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin. Wang, and Xindong Wu, A Data-Characteristic-Aware Latent Factor Model for Web Service QoS Prediction, IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp. 2525-2538, 2022. (CCF-A期刊，中科院一区，IF 8.9, ESI热点/高引论文) [2] Di Wu, Shengda Zhuo, Yu Wang, Zhong Chen, and Yi He, Online Semi-Supervised Learning with Mix-Typed Streaming Features, Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023 (CCF-A会议，Accept rate 19.6%) [3] Di Wu, Bo Sun, and Mingsheng Shang, Hyperparameter Learning for Deep Learning-based Recommender Systems, IEEE Transactions on Services Computing, 2023, doi: 10.1109/TSC.2023.3234623. (CCF-A期刊，中科院一区，IF 8.1) [4] Di Wu, Peng Zhang, Yi He, and Xin Luo, A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction, IEEE Transactions on Services Computing, 2022, doi: 10.1109/TSC.2022.3178543 (CCF-A期刊，中科院一区，IF 8.1) [5] Di Wu, Qiang. He, Xin. Luo, Mingsheng. Shang, Yi. He, and Guoyin. Wang, A posterior-neighborhood-regularized latent factor model for highly accurate web service QoS prediction, IEEE Transactions on Services Computing, vol. 15, no. 2, pp. 793-805, 2022. (CCF-A期刊，中科院一区，IF 8.1, ESI高引论文) [6] Di Wu, Xin Luo, Yi He, and MengChu Zhou, A Prediction-sampling-based Multilayer-structured Latent Factor Model for Accurate Representation of High-dimensional and Sparse Data, IEEE Transactions on Neural Networks and Learning Systems, 2022, 10.1109/TNNLS.2022.3200009. (中科院一区, CCF-B期刊, IF 10.4) [7] Di Wu, Mingsheng Shang, Xin Luo, and Zidong. Wang, An L₁-and-L₂-Norm-Oriented Latent Factor Model for Recommender Systems, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5775-5788, 2022. doi: 10.1109/TNNLS.2021.3071392. (中科院一区, CCF-B期刊, IF 10.4) [8] Di Wu, Yi He, Xin Luo, and MengChu Zhou, A Latent Factor Analysis-based Approach to Online Sparse Streaming Feature Selection, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, DOI: 10.1109/TSMC.2021.3096065 (中科院一区, CCF-B期刊，IF 8.7) [9] Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and MengChu Zhou, A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 7, pp. 4285-4296, 2021. (中科院一区, CCF-B期刊, IF 8.7, ESI高引论文) [10] Di Wu and Xin Luo, Robust Latent Factor Analysis for Precise Represen-tation of High-dimensional and Sparse Data, IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 4, 2021. (中国科技期刊卓越行动计划世界一流重点建设期刊, IF=11.8，中科院一区) [11] Di Wu, Yi He, and Xin Luo, A Graph-incorporated Latent Factor Analysis Model for High-dimensional and Sparse Data, Transactions on Emerging Topics in Computing, 2023, DOI: 10.1109/TETC.2023.3292866. (中科院二区，IF 5.9) [12] Di Wu, Xin Luo, Guoyin Wang, Mingsheng Shang, Ye Yuan, and Huyong Yan, A Highly-Accurate Framework for Self-Labeled Semi-Supervised Classification in Industrial Applications, IEEE Transactions on Industrial Informatics, 2018, 14 (3): 909-920. (中科院一区, IF 12.3) [13] Di Wu, Long Jin, and Xin Luo, PMLF: Prediction-Sampling-based Multilayer-Structured Latent Factor Analysis, In proceeding of the 2020 IEEE International Conference on Data Mining, ICDM, 2020. (长文, 接受率9.8%, CCF-B会议，core-rank A*) [14] Dianlong You, Jiawei Xiao, Yang Wang, Huigui Yan, Di Wu*, Zhen Chen, Limin Shen, and Xindong Wu, Online Learning from Incomplete and Imbalanced Data Streams, IEEE Transactions on Knowledge and Data Engineering, 2023, DOI: 10.1109/TKDE.2023.3250472. (CCF-A期刊，中科院一区，IF 8.9, *Corresponding Author) [15] Song Deng, Yujia Zhai, Di Wu*, Dong Yue, Xiong Fu, and Yi He, "A Lightweight Dynamic Storage Algorithm with Adaptive Encoding for Energy Internet", IEEE Transactions on Services Computing, 2023, doi: 10.1109/TSC.2023.3262635. (CCF-A期刊，中科院一区，IF 8.1，*Corresponding Author) [16] Song Deng, Jiantang Zhang, Di Wu*, Yi He, Xiangpeng Xie, and Xindong Wu, A Quantitative Risk Assessment Model for Distribution Cyber Physical System under Cyber Attack, IEEE Transactions on Industrial Informatics, 2022. DOI: 10.1109/TII.2022.3169456. (中科院一区, IF 12.3, *Corresponding Author) [17] Teng Huang, Cheng Liang, Di Wu*, and Yi He, "A Debiasing Autoencoder for Recommender System," IEEE Transactions on Consumer Electronics, 2023, doi: 10.1109/TCE.2023.3281521.(*Corresponding Author, IF 4.414, 中科院二区) [18] Dianlong You, Shina Niu, Siqi Dong, Huigui Yan, Zhen Chen, Di Wu*, Limin Shen, and Xindong Wu, Counterfactual explanation generation with minimal feature boundary, Information Sciences, vol 625, pp.342-366, 2023. (CCF-B期刊，中科院一区, IF 8.1, *Corresponding Author) [19] Dianlong You, Siqi Dong, Shina Niu, Huigui Yan, Zhen Chen, Shunfu Jin, Di Wu*, Xindong Wu, Local causal structure learning for streaming features, Information Sciences, vol 647, pp.119502, 2023. (*Corresponding Author, IF 8.1, 中科院一区, CCF-B) [20] Cheng Liang, Di Wu*, Yi He, Teng Huang, Zhong Chen, and Xin Luo, MMA: Multi-Metric-Autoencoder for Analyzing High-Dimensional and Incomplete Data, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2023. (CCF-B会议，Accept rate 24%,*Corresponding Author) [21] Bo Sun, Di Wu*, Mingsheng Shang, and Yi He, Toward Auto-learning Hyperparameters for Deep Learning-based Recommender Systems, International Conference on Database Systems for Advanced Applications. Springer, Cham, 2022. (CCF-B会议，*Corresponding Author) [22] Di Wu, Minsheng Shang, Xin Luo, Ji Xu, Huyong Yan, Weihui Deng, and Guoyin Wang, Self-training semi-supervised classification based on density peaks of data, Neurocomputing, 2018, 275:180-191. (中科院二区, IF 6) [23] Di Wu, Huyong Yan, Mingsheng Shang, Kun Shan, and Guoyin Wang, Water eutrophication evaluation based on semi-supervised classification: A case study in Three Gorges Reservoir, Ecological Indicators, 2017, 81: 362-372. (中科院二区, IF 6.9) [24]Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and Xindong Wu, A Data-Aware Latent Factor Model for Web Service QoS Prediction, In proceeding of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD, 2019. (CCF-C会议, 接受率24.1%，core-rank A)
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