within which the user interacts with the system (e.g., location, temporal information, devices, temperature, etc.). Personalization paradigm aims at adapting applications as much as possible to the user preferences and to the user context. Adaptation may concern several aspects, such as system reconfiguration, communication protocols, data sources selection, query reformulation, data layout, and user feedback handling. Data per- sonalization refers to the set of techniques that allow one to provide the user with the most relevant data, depending on his domain of interest, his data quality Personalization Paradigm in Service Delivery Platforms requirements, his location at the querying time, the time at which the data are required, the media used to supply these data or any other constraint related to data pricing, user privacy, or business policy. Works on personalization techniques concern, generally, a very few dimensions among these, and the proposed solutions are encapsulated as part of systems and applications features, thereby reducing their capabilities of extension and evolution. Actually, there is no effort devoted to the definition and production of personaliza- tion services which are generic enough to be used in many applications. Then, the personalization process is approached in different ways, depending on the applica- tions and on the technologies used. In information retrieval systems, the personalization is considered as a machine learning process based on user feedback [14,49]. The user is fully involved in the query evaluation which is conducted as a stepwise refinement process where the user can decide at each step which data he likes and which data he dislikes. From the log of this behavior, user profiles are elaborated and introduced as new filtering rules refining the results of further queries. In database systems, personalization is considered through two viewpoints: query language extension and user query expansion. Query language extensions, such as SQL/f [38] or PreferenceSQI [30], en