Role-Based Access Control Models
Computer
Exploring Context-aware Information Push
Personal and Ubiquitous Computing
A Data Model and Semantics of Objects with Dynamic Roles
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Massive Quasi-Clique Detection
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
A New Conceptual Clustering Framework
Machine Learning
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Mining Maximal Quasi-Bicliques to Co-Cluster Stocks and Financial Ratios for Value Investment
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
The role mining problem: finding a minimal descriptive set of roles
Proceedings of the 12th ACM symposium on Access control models and technologies
Fast exact and heuristic methods for role minimization problems
Proceedings of the 13th ACM symposium on Access control models and technologies
Migrating to optimal RBAC with minimal perturbation
Proceedings of the 13th ACM symposium on Access control models and technologies
Context-aware role-based access control in pervasive computing systems
Proceedings of the 13th ACM symposium on Access control models and technologies
Role Based Access Control with Spatiotemporal Context for Mobile Applications
Transactions on Computational Science IV
Optimal Boolean Matrix Decomposition: Application to Role Engineering
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A probabilistic approach to hybrid role mining
Proceedings of the 16th ACM conference on Computer and communications security
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
On the definition of role mining
Proceedings of the 15th ACM symposium on Access control models and technologies
An effective approach for mining mobile user habits
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Direction-based surrounder queries for mobile recommendations
The VLDB Journal — The International Journal on Very Large Data Bases
Construction and use of role-ontology for task-based service navigation system
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Context-aware role mining for mobile service recommendation
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Context-Aware Service Recommendation for Moving Connected Devices
ICCVE '12 Proceedings of the 2012 International Conference on Connected Vehicles and Expo
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Recent research has attempted to use role-based approaches to recommend mobile services to other members among the same group in a context dependent manner. However, the traditional role mining approaches originated from the domain of security control tend to be rigid and may not be able to capture human behaviors adequately. In particular, during the course of role mining process, these approaches easily result in over-fitting, i.e., too many roles with slightly different service consumption patterns are found. As a result, they fail to reveal the true common preferences within the user community. This paper proposes an online role mining algorithm with a residual term and an error term, that automatically group users according to their interests and habits without losing sight of their individual preferences and random errors. Moreover, to resolve the over-fitting problem, the authors relax the role definition in role mining mechanism by introducing quasi-roles based on the concept of quasi-bicliques. Most importantly, the new concept allows us to propose a monitoring framework to detect and correct over-fitting in online role mining such that recommendations can be made based on the latest and genuine common preferences. To the best of the authors' knowledge, this is a new area in service recommendation that is yet to be fully explored.