exts such as smart home intelligent agents [47] and context-aware information retrieval [27] for which the context refers usu- ally to spatial and temporal information of the interaction session as well as users' recent activities. Dey [23] defined the context as being any information that can be used to char- acterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user. Although context modeling has been addressed in many researches and commercial products, its semantics differs from one reference to another. The most known context model is the key-value model [8,40,42]. In this approach, the context is formalized as a conjunction of predicate of the form <attribute, value>. Stefanidis et al. [42] model the context with a set of predefined contextual parameters-a parameter consists in one or more predicate. In [28], the context (situation) is modeled in ER-model. Authors proposed a context-meta model based mainly on three dimensions: time, locality, and influences. The last one may con- cern human influences (e.g., physical state, mood) as well as surrounding influences (e.g., weather, luminosity). The context is also modeled through ontologies [46]. The ontology-based model enables sharing and reuse contextual information in groupware, and ubiquitous computing systems; further, it allows performing rea- soning on these models. There exist many other formalisms for modeling context such as XML based, object oriented, and logic based; an exhaustive survey of these modeling approaches can be found in [43]. By synthesizing these proposals and after clarifying our understanding of the context, we have proposed a multidimensional model, clearly separated from the profile model but related with an explicit and well-defined relationship called con- textualization. The following subsection gives an overview of our context model, while the next section details the contextualization concept. Multidimensiona