bes different preferences related to user interface modalities (e.g., presentation style, query results layout), temporal accessibility (e.g., moment at which queries are issued and results notified), query results size (e.g., first N objects), and SO on. Delivery modalities may depend on the media used: the same query results will not be presented in the same way depending on whether the media is a laptop, a Personal Digital Assistant (PDA), or a mobile phone. Delivery preferences are closely related to the context in which the user interacts with the application. Security and privacy dimension describes security rules and constraints that can be applied either to the data resulting from queries, to the queries themselves, to the user identity, or to the whole profile as a sensitive knowledge. Security dimension mainly refers to privacy policies as described in different standards such as P3P [CDE + 05] for example. Privacy preferences may force personalized applications to not keep track of some user knowledge (e.g., identity, banking data). Dimensions are described by ground attributes or complex attributes (called subdimensions) over which preference rules are defined. Due to the constraint of space, this chapter will not provide a detailed description of all attributes and sub- dimensions of the profile model. Figure 10.1 gives a flavor of the domain of interest dimension; detailed description can be found in [3]. The next subsection adds a little more emphasis on different types of preferences that the profile model allows one to define. Preferences were subject to many researches during the last 10 years [19,33,41]; they may be of several kinds: Example-based preferences consist in a set of content examples [14]. These examples can be put in positive (POS) and negative (NEG) sets representing respectively liked and disliked contents (e.g., POS-set = {<movie. genre=action>}). Weight-based preferences known also as quantitative preferences [32] consist in assigning scores to the objects (e.g., <movie.genre=a