Context aware role updating for IoT service recommendation
Zejin Zhu, Wang Jian · 2016
With the rapid development of Internet of Things (IoT) and mobile technologies, the service offerings available in the IoT and mobile environments are increasing dramatically.How to provide more intelligent and personalized services for users becomes a challengingissue.Several context aware service recommendation approaches have been reported to leverage roles to represent common knowledge within user communities, based on which intelligent services can be recommended for users.Prior studies on context aware role miningmainly focus on miningroles from a fixed data set of user behavior patterns, while most of them neglect the dynamic change of the input data.The frequent change of the user data will result in the changing of extracted roles, and how to efficiently update the extracted roles according to the change of the input user data remains a challenging issue.In this paper, towards this issue, we introduce a novel role updating approach of context aware role mining.Experiments show that compared with the algorithm that takes all the updated data as the input, our approach can significantly decrease the update time.