Yeondong Kim  Dojin Choi  Jongtae Lim  Kyoungsoo Bok  Jaesoo Yoo

10.5626/JOK.2020.47.1.61

social internet of things Interaction recommend cosine similarity

Recently, there has been extensive research on the social internet of things(Social IoT) that combines social networks and internet of things. Social IoT is integral for the connection between as well as for establishing relationships between users and objects for sharing information between objects or users. In this paper, we propose a method that recommends similar users by considering interaction between objects and users in the social IoT environments. The similar users can be found by analyzing the behavior of the users around the object. The proposed method improves the accuracy of similarity by calculating similarity in determining interests based on documents written by users in social networks. Finally, it recommends Top-N users as similar users based on the two similarity values. To show the superiority of the proposed method, we conducted various performance evaluations.