da- tion process benefits to any RS for many reasons: 1. Users' preferences/ratings change according to their contexts [8,42]. 2. The additive nature of the traditional RS [12] does not consider multiple ratings of the same content. 3. An RS may fail in providing some valuable recommendations as their simi- larity distance is uniformly applied to user preferences without analyzing the discrepancies introduced by the context. Based on the PAM, and following the efforts started by Adomavicius et al. [6,7] and Anand et al. [12], we proposed a CARS which is based on both user pro- files and contexts. In our approach, the same user who interacts from different contexts is provided with different recommendations. Our main contributions include the following: We present a general architecture for a CARS based on a set of personaliza- tion services. We extend the PAM with a new context learning service that enables concrete construction of users' contexts from their log files. * http://www.netflixprize.com Service Delivery Platforms The contextualization service proposed in [4] is improved by combining both support and confidence of ratings within a given context instead of considering their frequencies only. 10.4.2 CARS Requirements We focus on an RS that combines content-based techniques and collaborative filter- ing (CF). The content-based approach permits to learn users' profiles by analyzing content descriptors. Resulting profiles are, then, matched and compared to deter- mine similar users in order to make collaborative recommendations. The CF approach allows exploiting the ratings given by the Top K neighbors of the active user in order to derive the missing rating by aggregation function. Figure 10.13 gives a flavor of the CARS global architecture. This architecture is composed of the left-upper block that concerns knowledge acquisition and the right-bottom block that concerns personalized recommendation. 10.4.2.1 Knowledge Acquisition Processes This block is responsible for the acquisition and the management of 