A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
J. Wang, W. Li, and X. Luo, “A distributed adaptive second-order latent factor analysis model,” IEEE/CAA J. Autom. Sinica.. doi: 10.1109/JAS.2024.124371
Citation: J. Wang, W. Li, and X. Luo, “A distributed adaptive second-order latent factor analysis model,” IEEE/CAA J. Autom. Sinica.. doi: 10.1109/JAS.2024.124371

A Distributed Adaptive Second-Order Latent Factor Analysis Model

doi: 10.1109/JAS.2024.124371
More Information
  • loading
  • [1]
    Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30–37, Aug. 2009. doi: 10.1109/MC.2009.263
    H. Han, T. Zhang, M. L. Benton, C. Li, J. Wang, and J. Li, “Explainable t-SNE for single-cell RNA-SEQ data analysis,” bioRxiv: 2022.01. 12.476084, 2022.
    X. Luo, M. Zhou, S. Li, Y. Xia, Z. You, Q. Zhu, and H. Leung, “Incorporation of efficient second-order solvers into latent factor models for accurate prediction of missing QoS data,” IEEE Trans. Cybern., vol. 48, no. 4, pp. 1216–1228, Apr. 2018. doi: 10.1109/TCYB.2017.2685521
    A. H. Khan, S. Li, D. Chen, and L. Liao, “Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach,” Neuroco mput., vol. 400, pp. 272–284, Aug. 2020. doi: 10.1016/j.neucom.2020.02.109
    S. B. Joseph, E. G. Dada, A. Abidemi, D. O. Oyewola, and B. M. Khammas, “Metaheuristic algorithms for PID controller parameters tuning: Review, approaches and open problems,” Heliyon, vol. 8, no. 5, May. 2022.
    A. H. Khan, X. Cao, S. Li, V. N. Katsikis, and L. Liao, “BAS-ADAM: An ADAM based approach to improve the performance of beetle antennae search optimizer,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 461–471, Mar. 2020. doi: 10.1109/JAS.2020.1003048
    J. Tang, G. Liu, and Q. Pan, “A review on representative swarm intelligence algorithms for solving optimization problems: Applications and trends,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1627–1643, Oct. 2021. doi: 10.1109/JAS.2021.1004129
    F. Han, W. Chen, Q. Ling, and H. Han, “Multi-objective particle swarm optimization with adaptive strategies for feature selection,” Swarm Evol. Comput. , vol. 62, Apr. 2021.
    Q. Yang, W. Chen, J. Deng, Y. Li, T. Gu, and J. Zhang, “A level-based learning swarm optimizer for large-scale optimization,” IEEE Trans. Evol. Comput., vol. 22, no. 4, pp. 578–594, Aug. 2018. doi: 10.1109/TEVC.2017.2743016
    R. Lan, Y. Zhu, H. Lu, Z. Liu, and X. Luo, “A two-phase learning-based swarm optimizer for large-scale optimization,” IEEE Trans. Cybern., vol. 51, no. 12, pp. 6284–6293, Dec. 2021. doi: 10.1109/TCYB.2020.2968400
    J. Martens, “Deep learning via Hessian-free optimization,” in Proc. 27th Int. Conf. Mach. Learn., Jun. 2010, pp. 735–742.
    Y. Zhou, X. Luo, and M.-C. Zhou, “Cryptocurrency transaction network embedding from static and dynamic perspectives: An overview”, IEEE/CAA J. Autom. Sinica, DOI: 10.1109/JAS.2023.123450.
    Q. Liu, “Order-2 stability analysis of particle swarm optimization,” Evol. Comput., vol. 23, no. 2, pp. 187–216, Jun. 2015. doi: 10.1162/EVCO_a_00129
    F. M. Harper and J. A. Konstan, “The movielens datasets: History and context,” ACM Trans. Interact. Intell. Syst., vol. 5, no. 4, pp. 1–19, Jan. 2016.
    Y. Zhang, Z. Zheng, and M. R. Lyu “WSPred: A time-aware personalized QoS prediction framework for web services,” in Proc. 22nd IEEE Int. Symp. Softw. Rel. Eng. , vol. 15, no. 3, pp. 1334–1344, May–Jun. 2022.
    C. Lv, B. Peter, and K. Tsvi, “2nd Workshop on information heterogeneity and fusion in recommender systems (HetRec 2011),” Proc. 5th ACM Conf. Rec. Syst. Oct. 2011.
    K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, “Eigentaste: A constant time collaborative filtering algorithm,” Inf. Retrieval, vol. 4, no. 2, pp. 133–151, Jul. 2001. doi: 10.1023/A:1011419012209
    X. Luo, M. Zhou, Y. Xia, and Q. Zhu, “An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems,” IEEE Trans. Ind. Informat., vol. 10, no.2, pp. 1273–1284, May 2014.
    X. Luo, M. Zhou, S. Li, Z. You, Y. Xia, and Q. Zhu, “A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 3, pp. 579–592, Mar. 2016. doi: 10.1109/TNNLS.2015.2415257
    S. Sedhain, A. K. Menon, S. Sanner, and L. Xie, “AutoRec: Autoencoders meet collaborative filtering, in Proc. 24th Int. Conf. World Wide Web, May 2015, pp. 111–112.
    X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, “LightGCN: Simplifying and powering graph convolution network for recommendation,” in Proc. 43rd Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., Jul. 2020, pp. 639–649.
    W. Yue, Z. Wang, J. Zhang, and X. Liu, “An overview of recommendation techniques and their applications in healthcare,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 701–717, Apr. 2021. doi: 10.1109/JAS.2021.1003919


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(1)  / Tables(3)

    Article Metrics

    Article views (18) PDF downloads(5) Cited by()


    DownLoad:  Full-Size Img  PowerPoint