## 1 Introduction  
Communications and technology have become an important part of day-to-day activities. People interact with systems and applications through several devices and are willing to share information about preferences, interest and characteristics. Each user has different needs, preferences and interests that can change in an instant. Therefore, personalizing and adapting product or service that fulfills user's present need adequately is a challenging task, but it is a must to allow the growth and penetration of ubiquitous applications. Sometimes, the user has to settle for stereotyped products and services. If the user does not get what he/she wants or he/she has to spend more time searching for a suitable alternative to his/her needs, or will stop using the application. In order to change the behavior of devices, systems and applications, and to achieve a more personalized delivery of product and services, it is necessary to model the user's profile [1,2]. Currently, many service providers have user-adaptive systems and are trying to model the user for adaptation and customization, but these efforts are frequently isolated and repeated by each supplier. In user-centered computing environments, the key for personalization is to collect user's information and construct a ubiquitous user model [3] that represents the user's characteristics, preferences, interests, intentions and needs as accurate as possible. Service providers build user models using different well known techniques [4], collect explicit information provided by the user, implicit information obtained from embedding Human-Machine Interaction (HMI) technologies, applications and devices and indirect information obtained from advanced sensors and complementary systems. The computational effort of building a user model is repeated across different platforms, applications and systems in various domains. The user has to invest time in setting parameters of devices and services repeatedly, which leads to overabundance of information, inconsistency, redundancy and repeated configurations. The information of the user is scattered over different user models, so each application has just a narrow understanding of the user, given by some features of the user profile. Some researchers in the user modeling community [3,5,7, 6] envision the need to share user models' information between applications in order to enhance and integrate the knowledge of the user. With a better understanding of the user, adaptive systems can provide better service, content and interface personalization and adaptation. Sharing and reusing information between models can bring advantages for profile providers and profile consumers; it helps dealing with the above mentioned problems, provides enrichment for the user models and frees the user from the need to repeat the configuration process, but gathering distributed user information from heterogeneous sources implies interoperability problems [7]. The lack of interoperability between adaptive systems is due to the syntactic and semantic heterogeneity of the different user models. Several techniques have been proposed to deal with the lack of interoperability, and support user modeling for cross-system personalization [5,8,9]. The two mayor approaches for user modeling interoperability are the definition of standard ontologies that provide a common understanding among multiple systems or a conversion approach using mediation techniques to provide mappings between the different user models. The development of a standard ontology to provide a static common understanding for multi-application personalization is not a feasible solution, it has become clear in recent years, that theuser model representation must be able to evolve over time [8]. The ubiquitous user model must be flexible to allow personalization of frequently used and new applications in different possible contexts. [PERSON] [6] gives a definition of mediation to introduce cross-system personalization using techniques that match and integrate user models.