on Algorithm in CARS: The user history H on which the contex- tualization service is based is obtained by transforming the user log file L (Table 10.1). A sample of this history is presented in Table 10.3. User behavior is captured in a relational table where each row is of the form: <id, Context_id, Content_id, Feature, Action_type> express- ing that a user consumed a content (item) having some features (predicates) in a specific way (Action type) within a given context (context_id Action_type specifies whether the action applied by a user when consuming the content is positive (e.g., Buy) or negative (e.g., Ignore). Content_id and Table 10.3 A Sample of User History Context-Cluster Content Feature C-Action Others Avatar.avi Action SDP.pdf Service Avatar.avi Sci-Fic Service Delivery Platforms Features are obtained by transforming the URI of the consumed content (which is a log file field) into more significant information (e.g., http://www.imdb.com/ title/tt0448157/ becomes Title: Hancock, Genre: Action, Comedy, Crime). Context_id specifies the context cluster within which the content was con- sumed; it is computed by the context discovery service. The algorithm of contextual profile construction captures for each user rating (more precisely, predicate), its importance in each user context. Notice that, in each context, user activities may be of two kinds: positive (belongs to liked contents) and/or negative (relative to disliked contents), according to the type of actions a user applied on contents. Therefore, the importance of each predicate must be cap- tured in both positive and negative activities. As presented in [4], the importance of a predicate pri within a context Cj is cap- tured by computing its frequency in this context. However, we claim that the fre- quency, alone, does not reflect the real importance of a predicate for one user. Therefore, we propose to model the importance of each predicate pri for the positive (resp. negative) activity within a given context as an association rules of the form 