A social trust based friend recommender for online communities “invited paper”

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.