Social Recommendation Algorithm Dynamically Adaptable to User Profiling for SNS

Weimin Li, Yikai Ni, Minye Wu, Zhengbo Ye, Qun Jin

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

    1 Citation (Scopus)

    Abstract

    With the development of social network services, the user relation spectrum of the social network has exceeded our imagination. Hence, personalized recommendation algorithms are adopted in many social networking sites to help users find their potential friends and related information more quickly and conveniently. In this paper, we discuss the weaknesses of current algorithms, and propose a user profile integrated dynamic social recommendation algorithm in order to overcome those limitations. Finally, through the experiment on Weibo dataset, it can conclude that the proposed algorithm outperforms traditional approaches in terms of accuracy and stability.

    Original languageEnglish
    Title of host publicationProceedings - 2014 2nd International Conference on Advanced Cloud and Big Data, CBD 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages261-266
    Number of pages6
    ISBN (Print)9781479980857
    DOIs
    Publication statusPublished - 2015 Aug 3
    Event2nd International Conference on Advanced Cloud and Big Data, CBD 2014 - Huangshan, Anhui, China
    Duration: 2014 Nov 202014 Nov 22

    Other

    Other2nd International Conference on Advanced Cloud and Big Data, CBD 2014
    CountryChina
    CityHuangshan, Anhui
    Period14/11/2014/11/22

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    Keywords

    • Collaborative filtering
    • Dynamic recommendation
    • Profile matching
    • Social network

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems

    Cite this

    Li, W., Ni, Y., Wu, M., Ye, Z., & Jin, Q. (2015). Social Recommendation Algorithm Dynamically Adaptable to User Profiling for SNS. In Proceedings - 2014 2nd International Conference on Advanced Cloud and Big Data, CBD 2014 (pp. 261-266). [7176103] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBD.2014.42