ural issues Allow partial or full usage of this process such that each application can use part or entire knowledge defined by the meta model The chapter is organized as follows. Section 10.2 gives a global view of our meta model that captures the semantics of profile and context. Section 10.3 describes the PAM and its personalization services. Section 10.4 presents the PAM application to context-aware recommender systems (CARS). Section 10.5 concludes this chapter with the summary of the main concepts introduced and further research on PAM and CARS. 10.2 Personalization Meta Data: Profiles and Contexts Profiles and contexts are the main concepts used by modern applications (e.g., e-commerce, recommender systems, and publish/subscribe systems) to adapt con- tent delivery services to the users' needs and preferences. While the two terms are intensively used in many papers and products, their definitions are specific to each application and the relationship between the two remains unclear. Few applications are profile based while others are claimed to be context aware. Some others deal with contextual or situational preferences. Although there is some stan- dardization effort (e.g., P3P, CC/PP, CSCP, UAPref) and application deployments (e.g., iGoogle, MyYahoo), these concepts need to be clearly defined both in their semantics and usage. This chapter is a step forward in this concern. It aims at pro- viding disjoint definitions of the two concepts through a multidimensional meta modeling. 10.2.1 Profile Modeling A profile is a user model which contains all knowledge that characterize this user, regardless of any application or any technology environment. A profile may cover, for a given domain of interest, all what the user likes or dislikes. A user profile can be seen as a set of organized prescriptions that allow applications to filter and rank any content they deliver to the user. A profile may be either a user property (external profile) or a service provider property (hidden profile). In the former case, the us