9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

Research Article

A Social Trust Based Friend Recommender for Online Communities

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2013.254213,
        author={Surya Nepal and Cecile Paris and Payam Aghaei Pour and Sanat Bista and Jill Freyne},
        title={A Social Trust Based Friend Recommender for Online Communities},
        proceedings={9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing},
        publisher={ICST},
        proceedings_a={COLLABORATECOM},
        year={2013},
        month={11},
        keywords={social networks online communities social trust recommender system friend recommender},
        doi={10.4108/icst.collaboratecom.2013.254213}
    }
    
  • Surya Nepal
    Cecile Paris
    Payam Aghaei Pour
    Sanat Bista
    Jill Freyne
    Year: 2013
    A Social Trust Based Friend Recommender for Online Communities
    COLLABORATECOM
    IEEE
    DOI: 10.4108/icst.collaboratecom.2013.254213
Surya Nepal1, Cecile Paris1, Payam Aghaei Pour1, Sanat Bista1,*, Jill Freyne1
  • 1: CSIRO Computational Informatics
*Contact email: Sanat.Bista@csiro.au

Abstract

Recommendations to connect like-minded people can result in increased engagement amongst members of online communities, thus playing an important role in their sustainability. We have developed a suite of algorithms for friend recommendations using a social trust model called STrust. In STrust, the social trust of individual members is derived from their behaviours in the community. The unique features of our friend recommendation algorithms are that they capture different behaviours by (a) distinguishing between passive and active behaviours, (b) classifying behaviours as contributing to users’ popularity or engagement and (c) considering different member activities in a variety of contexts. In this paper, we present our social trust based recommendation algorithms and evaluate them against algorithms based on the social graph (such as Friends-Of-A-Friend). We use data collected from the online CSIRO Total Wellbeing Diet portal which has been trialled by over 5,000 Australians over a 12 week period. Our results show that social trust based recommendation algorithms outperform social graph based algorithms.