From user graph to Topics Graph: Towards twitter followee recommendation based on knowledge graphs

Twitter is a rapidly growing microblogging platform that allows its users to send and read short messages, called tweets. Because of the fact that a user's timeline consists of the latest tweets of their followees (users that they are following), followee recommendation is a problem of significant importance. In this work we propose a followee recommendation approach, which takes advantage of the increasing amount of available social data and specifically the semantic relatedness of topics that interest users. In order to accomplish this we use a Topic Graph, containing a wide variety of topics that will be used for the recommendation process. Today knowledge graphs provide a solid basis for us to construct a full and reliable Topic Graph. Our approach takes advantage of the semantic information retrieved from users' tweets, in order to build an interest profile for each user. Then we use graph theory algorithms in order to calculate user interest similarity using the Topic Graph.