knowledge a CARS needs to process recommendations. This knowledge is grouped into four enti- ties user profiles, content descriptors, contexts, and contextual mappings (relation- ships between profiles and contexts). Profiles and contents (items) were subject to extensive research on how they are generated and modeled. Thus, we focus in this chapter on the two remaining entities, or more precisely on the processes that gener- ate them: (1) context acquisition from log files and (2) discovery of relationships between these contexts and user profiles elements (called contextual profiles). Knowledge acquisition processes Profile Context acquis. Profiles Contextualization Contexts acquis. Logs/ feedback Contextual profiles Content Recommender Contents Recomm. acquis. engine lists Personalized recommendation Figure 10.13 Global architecture of CARS. Personalization Paradigm in Service Delivery Platforms 10.4.2.2 Personalized Recommendation This block encompasses actual RS operations. The recommender engine (RE) takes as inputs (1) the contextual profiles generated in the knowledge acquisition block and (2) the current context of the target user in order to compute a list of contextual recommendations. The behavior of the target user when consuming these recom- mendations is stored in log files on which acquisition processes are based to update profiles, contexts, and their mappings; hence, closing the cycle between knowledge acquisition and their exploitation. The design of such CARSs is driven by the following requirements: 1. Distinguish between user profiles and contexts to guarantee a well- understanding definition and evolution of both entities. Enable the discovery of users' contexts through a concrete approach and widely available data (e.g., standard log files). 3. Take into account both user profile and context in the recommendation pro- cess such that a user who interacts from different contexts would be provided with appropriate recommendation lists. 4. Propose an efficient profile matching approach to solv