bset of predicates that should be fully or partially satisfied (top K predicates), discriminating between query results (top K results), adapting data delivery to the context (interaction media, spatio-temporal position), and SO on. The next subsections introduce, respec- tively, the main dimensions of our profile model as well as the various ways prefer- ences are specified within these dimensions. 10.2.1.1 Multidimensional Modeling of Profile The multidimensional view of a profile is not new. Several attempts have already introduced various dimensions such as personal data [10], data delivery preferences [24], data quality [16], and security preferences [20]. We capitalize on these approaches and generalize them to achieve a certain level of genericity where existing profile models are simple instances of our model, and where each new personalized application can easily elaborate its own profile model. The ultimate goal is to provide a framework where personalized applications based on different profile models can interoperate with each other, offering the user an expanding personalization environment. A brief description of each dimension of our model is given below. Personal data dimension groups attributes and preferences related to the user himself, that is, his identity, demographic data, professional data, health-care data, and SO on. In some information systems, personal data will be used to filter query results with respect to the age of the user, his gender, or his area of work. In others, personal data are useless for information filtering itself but they are still useful as an Service Delivery Platforms "exchange currency" between the user and the information provider. This is the case in many e-commerce applications and Web-based systems that collect personal data for statistics purpose or for publicity dissemination. Domain of interest is the central dimension of the user profile. It groups all attri- butes and preferences related to general needs of a given user. The domain of inter- est may descr