instantiated in a specific content delivery platform (CDP) through recommender system application. 10.4 Application: Context-Aware Recommender System Once profiles, contexts, and contextual mappings are modeled and instantiated, they are ready to be used by any personalized application. This section illustrates the use of these concepts within an application scenario borrowed from the recom- mender system (RS) in content delivery environment. We show through this section how PAM services can be deployed in order to model a context-aware recommender system (CARS). Personalization Paradigm in Service Delivery Platforms 10.4.1 Why Is Context Important for Recommender Systems? The importance of an RS is now well established. Netflix organizes a contest* in which one million dollar is offered for any better recommendation engine. This contest shows, on the one hand, the importance that industrials give to an RS, and, on the other, that better recommendations worth a lot. Two important questions arise from the Netflix contest: (1) What is a better RS? and (2) How can a system provide the best recommendations? In our vision, a better RS is the one that delivers recommendations that best match with users' preferences, needs, and hopes at the right moment, in the right place, and on the right media. This cannot be achieved without designing an RS that takes into account all information and parameters that influence user's ratings. These information may concern demographic data, preferences about user's domain of interest, quality and delivery requirements, as well as the time of the interaction, the location, the media, the cognitive status of the user, and his availability. This knowledge is organized into the two concepts of user profile and context. The user profile groups information that characterizes the user himself, while the context encompasses a set of features that describe the environment within which the user interacts with the system. We claim that taking into account both profiles and contexts in a recommen