Context-Aware User Model for Personalized Services

With the advent of ubiquitous computing environments, a significant amount of work has been devoted to context-aware. We proposed the context-aware user model, which is aware of its user's situation and recommends personalized services based on this information. This paper focuses on how our algorithms can be applied to a variety of environment easily and evolve user model without previously known information since the concerns of privacy. The proposed user model consists of context and action pairs. Using context-aware user model, we learn user behavior pattern in the situation, which is captured by union of contexts according to current environments. In order that our user model applies to new environment, only context profiles are required to be defined without any modification to model. Moreover we evolved user model through interactions between users and mobile devices using learning mechanism. Finally, we evaluate the precision of proposed approach using simulation with data sets of UCI depository.