A novel personalized recommendation algorithm based on trust relevancy degree

Weimin Li, Heng Zhu, Xiaokang Zhou, Shohei Shimizu, Mingjun Xin, Qun Jin

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The rapid development of the Internet and ecommerce has brought a lot of convenience to people's life. Personalized recommendation technology provides users with services that they may be interested according to users' information such as personal characteristics and historical behaviors. The research of personalized recommendation has been a hot point of data mining and social networks. In this paper, we focus on resolving the problem of data sparsity based on users' rating data and social network information, introduce a set of new measures for social trust and propose a novel personalized recommendation algorithm based on matrix factorization combining trust relevancy. Our experiments were performed on the Dianping datasets. The results show that our algorithm outperforms traditional approaches in terms of accuracy and stability.

    Original languageEnglish
    Title of host publicationProceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages418-422
    Number of pages5
    ISBN (Electronic)9781538675182
    DOIs
    Publication statusPublished - 2018 Oct 26
    Event16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018 - Athens, Greece
    Duration: 2018 Aug 122018 Aug 15

    Other

    Other16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018
    CountryGreece
    CityAthens
    Period18/8/1218/8/15

    Fingerprint

    Personalized Recommendation
    Social Networks
    Factorization
    Data mining
    Matrix Factorization
    Internet
    Electronic Commerce
    Sparsity
    Data Mining
    Experiments
    Experiment
    Social networks

    Keywords

    • Collaborative filtering
    • Matrix factorization
    • Personalized recommendation
    • Trust

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Artificial Intelligence
    • Information Systems and Management
    • Safety, Risk, Reliability and Quality
    • Control and Optimization

    Cite this

    Li, W., Zhu, H., Zhou, X., Shimizu, S., Xin, M., & Jin, Q. (2018). A novel personalized recommendation algorithm based on trust relevancy degree. In Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018 (pp. 418-422). [8511927] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00084

    A novel personalized recommendation algorithm based on trust relevancy degree. / Li, Weimin; Zhu, Heng; Zhou, Xiaokang; Shimizu, Shohei; Xin, Mingjun; Jin, Qun.

    Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 418-422 8511927.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Li, W, Zhu, H, Zhou, X, Shimizu, S, Xin, M & Jin, Q 2018, A novel personalized recommendation algorithm based on trust relevancy degree. in Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018., 8511927, Institute of Electrical and Electronics Engineers Inc., pp. 418-422, 16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018, Athens, Greece, 18/8/12. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00084
    Li W, Zhu H, Zhou X, Shimizu S, Xin M, Jin Q. A novel personalized recommendation algorithm based on trust relevancy degree. In Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 418-422. 8511927 https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00084
    Li, Weimin ; Zhu, Heng ; Zhou, Xiaokang ; Shimizu, Shohei ; Xin, Mingjun ; Jin, Qun. / A novel personalized recommendation algorithm based on trust relevancy degree. Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 418-422
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    abstract = "The rapid development of the Internet and ecommerce has brought a lot of convenience to people's life. Personalized recommendation technology provides users with services that they may be interested according to users' information such as personal characteristics and historical behaviors. The research of personalized recommendation has been a hot point of data mining and social networks. In this paper, we focus on resolving the problem of data sparsity based on users' rating data and social network information, introduce a set of new measures for social trust and propose a novel personalized recommendation algorithm based on matrix factorization combining trust relevancy. Our experiments were performed on the Dianping datasets. The results show that our algorithm outperforms traditional approaches in terms of accuracy and stability.",
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