Context-aware role mining for mobile service recommendation

Authors:
Jian Wang;Cheng Zeng;Chuan He;Liang Hong;Liang Zhou;Raymond K. Wong;Jilei Tian
Affiliations:
University of New South, Wales, Australia;University of New South, Wales, Australia;University of New South, Wales, Australia;University of New South, Wales, Australia;Wuhan University, China;University of New South, Wales, Australia;Nokia Research Center, Beijing, China
Venue:
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Year:
2012

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Abstract

Finding and recommending suitable services for mobile users are increasingly important due to the popularity of mobile Internet. While recent research has attempted to use role-based approaches to recommend services, role discovery is still an ongoing research topic. In particular, numerous role mining approaches have been proposed in the context of RBAC (Role-Based Access Control) in which only two dimensions of parameters are considered (i.e., ); and the notion of context-awareness has been disregarded. This paper proposes a context-aware role mining method to automatically group users according to their interests and habits, such that popular mobile services can be recommended to other members in the same group in a context dependent manner. Experiments show that our approach is efficient and practical for mobile service recommendation.