urn TOPK 10.4.5.1 Aggregating Neighbor Ratings Once Top k neighbors of the active user Ua are determined, ratings they gave to the item (It) to be recommended must be aggregated. The result of this aggregation allows Personalization Paradigm in Service Delivery Platforms deciding whether it is relevant or not to recommend It to the user Ua There exist sev- eral techniques for aggregating these ratings [12,15]. The one we considered is the Weighted Mean Aggregation (Equation 10.4) where the contribution of each neighbor rating is weighted with the similarity between this neighbor and the active user Ua. Rating(u,It) Match(u,ui) Rating(u;,It), (10.4) where a is a normalizing factor. According to the value of the aggregated rating, the RS decides whether It could be recommended or not. 10.5 Conclusion In this chapter, we investigated the personalization paradigm in SDPs through user profiles and contexts. We showed that profile and context are different concepts that cover different knowledge. The concept of profile represents user knowledge regardless to any personalized application, while the concept of context represents specific environments that an application is able to recognize independently of any user. We provided generic models for both concepts. These models can be used independently of each other or in conjunction depending on whether application is only profile based, context based, or profile and context based. We claim that these two concepts are fundamental baselines to build personalized applications or inter- operate between existing ones; therefore, a generic model synthesizing both models is abstracted from the previous ones and a set of customization and instantiation operations have been defined to respectively adapt the generic models to specific user/application needs and to generate concrete instances. We, also, proposed a PAM that encompasses profile and context meta models that serve as a foundation for the interoperability between services and applications. The PAM provides a set of gene