the current context of the user. It corresponds to the subset of user profile that CARS processes to provide contextual recommendations. 10.4.2.2.2 Simple Scenario The scenario below gives an intuition about the way CARS provides users with contextual recommendations. Figure 10.14 focuses on the RE sketched in Figure 10.13. Dashed arrows represent the inputs of the RE. Recommendation request Parser Content Active context Filter Contextual Rating profiles Context- context (RC) matcher Context- Context Candidate matcher clusters Context contextual cluster profile Binding Binding Contextual profile Candidate Operational Profile-matcher operational profile profiles Top k neighbors Rating aggregation Figure 10.14 Contextual CF-RS. Personalization Paradigm in Service Delivery Platforms When recommendations are requested, explicitly by the users (here John) or implicitly by the applications, the RE computes them following three processes A, B, and C shown in Figure 10.14. First of all, a parser extracts the active context (ca) of the user, and designates the candidate content to recommendation (step A1). In step B1, the active context is matched with all context clusters in order to determine the one to which Ca belongs. This context cluster is used in step B2 for binding John's contextual profile and producing his operational profile. At the same time, the RE looks for the candidate neighbors of John (process C). A candidate neighbor is a user who rated the content to be recommended (filtering: step C1), in a context Cr similar to the active context Ca (step C2). In step C3, the contextual profile of each candidate user is bound to its corresponding context Cr, resulting in a set of operational profiles. Step A2 synchro- nizes the two processes B and C. It consists in determining the top k neighbors of John among the candidate users. This is done by capturing the similarity between each candidate profile and John profile; only top k similar profiles are retained. Step A3 aggregates ratings given to the recommended cont