+> (resp.(pr; ->). Then, the importance of pri is obtained by combining both the support (sp) and the confidence (cf) of its corresponding rules. The meaning of these two metrics is given through the following example. Example. Assume that John is provided by 1000 movie recommendations in a given context, 701 are action movies, and 299 are comedy movies. Among the 701 action movies, John liked 503 and disliked 198, whereas he liked the 299 comedy movies. Then, supports (frequencies) of corresponding rules are support (<genre=action>, +)=(503/1000), support <genre=action>,-): (198/1000), and support (<genre=comedy> +) = 299/1000, support(<genre=comedy>,): = (0/1000), which clearly shows that the user prefers action movies to comedy movies, or in other words, that <genre=action> predicate is more important than <genre=comedy> one. However, John's behavior can be interpreted differently: among 299 comedy movies, John liked all of them, while he liked only 503 action movies among 701, that is, comedy genre becomes more important than action genre. This semantics is captured by confidences: confidence(<genre=action>,+) = (503/701), confidence (<genre=action>, =(198/701) confidence(<genreaction>, +) = =(198/701) and confidence(<genre=comedy>,+) = (299/299). To consider both frequencies (supports) and confidences, many functions may be used to aggregate them. The most natural way to do that is to compute the mean between support and confidence as shown in the following equation: (10.1) where * takes one value among {+ , -} at a time. Personalization Paradigm in Service Delivery Platforms Algorithm 2 shows the way contextual profiles are constructed based on user's interaction histories. The contextual profile constructed in algorithm 2 allows positioning each pro- file rating in each context through a score. The next section discusses the manner this score is exploited to make contextual recommendations. 10.4.4 Binding and Matching Services This section details runtime services. They constitute an answer to the last two