An integrated recommendation approach based on influence and trust in social networks

Weimin Li, Zhengbo Ye, Qun Jin

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

    5 Citations (Scopus)

    Abstract

    In real human society, influence on each other is an important factor in a variety of social activities. It is obviously important for recommendation. However, the influence factor is rarely taken into account in traditional recommendation algorithms. In this study, we propose an integrated approach for recommendation by analyzing and mining social data and introducing a set of new measures for user influence and social trust. Our experimental results show that our proposed approach outperforms traditional recommendation in terms of accuracy and stability.

    Original languageEnglish
    Title of host publicationLecture Notes in Electrical Engineering
    PublisherSpringer Verlag
    Pages83-89
    Number of pages7
    Volume309 LNEE
    ISBN (Print)9783642550379
    DOIs
    Publication statusPublished - 2014
    Event9th FTRA InternationalConference on Future Information Technology, FutureTech 2014 - Zhangjiajie
    Duration: 2014 May 282014 May 31

    Publication series

    NameLecture Notes in Electrical Engineering
    Volume309 LNEE
    ISSN (Print)18761100
    ISSN (Electronic)18761119

    Other

    Other9th FTRA InternationalConference on Future Information Technology, FutureTech 2014
    CityZhangjiajie
    Period14/5/2814/5/31

    Keywords

    • influence
    • recommendation algorithms
    • similarity
    • social trust

    ASJC Scopus subject areas

    • Industrial and Manufacturing Engineering

    Cite this

    Li, W., Ye, Z., & Jin, Q. (2014). An integrated recommendation approach based on influence and trust in social networks. In Lecture Notes in Electrical Engineering (Vol. 309 LNEE, pp. 83-89). (Lecture Notes in Electrical Engineering; Vol. 309 LNEE). Springer Verlag. https://doi.org/10.1007/978-3-642-55038-6_13