Online Role Mining without Over-Fitting for Service Recommendation

Due to the popularity of smartphones, finding and recommending suitable services on mobile devices are increasingly important. 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 that automatically group users according to their interests and habits without losing sight of their individual preferences. Moreover, to resolve the over-fitting problem, we relax the 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 our knowledge, this is a new area in service recommendation that is yet to be fully explored.