User Clustering Based Social Network Recommendation
Chen Ke · Chinese Journal of Computers · 2013
Comparing to the ordinary social networks services(SNS),the twitter-like weak-relationship based social networks are observably heterogeneous.By classifying the nodes into users(subscriber) and subjects(publisher),the goal of recommendation systems over this kind of networks is basically recommending the subjects to the users for subscription.Moreover,the data sparseness and cold-start scene always exists in these microblog networks.In this paper,we propose GCCR,a hybrid method combining both graph-summarization and content-based algorithms by a two-phase user clustering approach,which can recommend subjects according to user interests.With respect to other methods,the GCCR algorithm could generate better recommendation result in sparse datasets and cold-start scenarios.In additional,by separating the task into offline and online parts,GCCR works more efficiently online by using the pre-processed offline results.We use real data set from existing social networks to evaluate GCCR along with base-line methods.Moreover,an analysis of the parameters is given for evaluating their impacts on recommendation results.