Personalized recommendation via rank aggregation in social tagging systems

This paper presents how to exploit rank aggregation approach to make personalized recommendation in social tagging systems. For this, some basic methods based on different principles and features, such as user-based collaborative filtering (CF), graph-based method and social-based CF are first introduced. Then, we specially adjust and optimize these methods to produce better results. Then, we exploit rank aggregation approaches to integrate these basic models to form hybrid recommenders. We experiment our methods on Lastfm dataset. And by solid experiments, our proposed hybrid models achieve optimal recommendation accuracy leveraged by the superiority of sub-models.