eat 7: Eq1a:=E 8 : for all context instance tject do 9: assign tj to its closest cluster C*EC such that Acie C, MATCH(tj,C1) MATCH (tj, C*) 10: for all cluster CiEC do 11: update its mean m - NewMean (Ci) 12: compute MATCH (t,m)) 13: until E>E old 14: return C Algorithm 1 starts by extracting context instances from the user log file (Line 2). In Line 3, AHC is applied on these instances in order to compute the initial Personalization Paradigm in Service Delivery Platforms k clusters. Then, from Lines 4 to 13, k-means is applied as an iterative relocation algorithm in order to improve the initial clustering obtained with AHC. The algo- rithm iterates until the error E achieves its minimal value. 10.4.3.2 Contextualization Service Contextualization process takes as input a user profile P and the user history H corresponding to the user feedback in contexts C discovered previously. It returns a set of contextual mappings M representing dependencies between elements in Pu and contexts in C. With regard to CARS, this set of mappings will constitute a contextual profile CPu u defined below. Contextual Profile: In the traditional RS, a user profile is a set of ratings Pu={R1, , R}, where each rating R is composed of a predicate pri and a rate (weight) Vis that is, = The rate Vi is a real number expressing the impor- tance of the predicate pri for the user. pri is a triplet <concept, operator, value>, for example, Genre= "Drama". Concepts may be items (content) that a user consumed (CF-RS), features of these items (CBF-RS) or both of them (Hybrid-RS). Based on this profile, a contextualized user profile CP is defined as a set M of contextual mappings which relates user ratings to a set of contexts C: Each contextual mapping Mij is defined by a rating R, a context Cj> and a score Sij The score Sij is a real number expressing the importance of taking into account the Rating R when the user interacts from the context Cj. Hereafter, we describe the way a contextual user profile is constructed using log files. Contextualizati