extual score >ia* In this way, the higher the importance of the contextual score of a given pri is, the more is its related rating Vi preserved. One other possible exploitation of these numerics is to consider only (sia) in the recommendation process. This means that the initial user rating ri is ignored. 10.4.4.2 Matching Services The matching service aims at measuring the semantic similarity between profiles, contexts, and contents. According to CARS need, we focus on two kinds of match- ing services: the profile/profile matcher that captures the similarity between two profiles and the context/context matcher that measures the similarity between two contexts. 10.4.4.2.1 Profile-to-Profile Matcher This matcher takes as input two operational user profiles OPu and OP. u2 Each profile contains a set of contextual ratings CR = <item, w>. Thus, we will use one of the well-known vector metrics, such as cosine, Pearson's or Spearman's correlation, to measure the similarity between the two profiles. However, before applying these metrics, we must ensure that the two compared profiles OPu and OP2 are homogeneous. Two profiles OPu and OP2 are said homogeneous iff: Under a certain order, OD, OPu (i) item = OPu2(i).item Due to the well-known problem of rating sparsity, profiles are rarely homo- geneous. Thus, using Generalized Cosine Max metric [11] is one possible way to efficiently overcome the sparsity problem. Similarly, we propose a technique that uses ontologies to homogenize the two compared profiles. The main idea is based on the assumption that a user likes similarly similar items. Example. Consider two PhD students, John: (<Drama, 0.2>, <Action, 0.8>, <War, 0.6>) and Julia: (<Drama, 0.9>, <Action, 0.3> <War, 0.4>, <Social, 0.7>), with their Personalization Paradigm in Service Delivery Platforms respective profiles related to movie genres. To homogenize John and Julia profiles, we have to predict the rating r1 John would give to Social movies. r1 is estimated as fol- lows: r1 = Similarity (Social, C, O) X rating(c,