ibe the user's expertise or qualification in a specific field as well as the main object types he is interested in. It can be defined in different ways depending on the application needs (Figure 10.1). For instance, in information retrieval, the domain of interest is usually described by a set of possibly weighted keywords [39] or ontology graphs [22], while in database field, it is commonly described by a set of predicates [32] or expressions in a given formalism such as Horn clauses [21] or utility functions [18]. In some applications, the domain of interest is represented by the history of the user interactions with the system. This includes examples of ele- ments to which are associated the actions performed by the user on them. Knowledge defined in the domain of interest is mainly used to reformulate user queries, either by term substitution, by complementing the queries with new selection predicates, or by introducing orders between predicates. Data quality dimension is one of the most important issues in data personaliza- tion. Most of the user preferences relate to data accuracy, data freshness, data con- sistency, and SO on. Data quality does not only concern data values, but also data sources (e.g., confidence, update frequency) and data derivation process (e.g., response time, reliability). Attributes and preferences of the quality dimension can be used by the query processor or more generally by the data management system to filter the accessed data sources when their number is significantly high [35] or to find a good balance between data freshness and response time [16]. Profile Profile dimension Personal data Data quality Domain of interest Data delivery Security privacy Keywords vector Ontology DisjFormulas History Concepts graph ConjPredicates Examples list Concept Predicate Example SemLink Axicm Unique list Between Actions Content Attribute Operator Value Recurrent list Figure 10.1 Domain of interest dimension. Personalization Paradigm in Service Delivery Platforms Data delivery dimension descri