Abstract: An approach is provided for training a device to associate user, object or device context information with a user defined context. The context information platform receives recorded context information from a device. The associated recorded context information is then associated with the context to enable training of a context model associated with the context.

Abstract: An approach is provided for characterizing user behavior patterns. The behavior pattern platform receives a plurality of context records from a device. Next, the behavior pattern platform places one or more contexts from the context records. Then, the behavior pattern platform places the contexts into one or more context groups. Then, the behavior pattern platform receives interaction data from the device, associates the context groups with the interaction data, and determines a behavior pattern based, at least in part, on the association of the context groups and the interaction data.

Abstract: An approach is provided for a holistic framework to model user item rating with user generated tag information. A tagging manager determines one or more tags associated with one or more items, wherein the one or more tags are generated by one or more users. The tagging manager processes and/or facilitates a processing of the one or more tags to cause, at least in part, a generation of one or more semantic spaces. The one or more semantic spaces and/or one or more semantic concepts within the one or more semantic spaces represent one or more groupings of the one or more tags. The tagging manager determines one or more probability parameters that the one or more tags, the one or more users, the one or more items, or a combination thereof are associated with respective ones of the one or more semantic concepts in the semantic spaces.

Abstract: Techniques for collaborative context recognition include determining a plurality of groups of user types. Each different group is associated with a corresponding different range of values for an attribute of a user. First data is received, which indicates context data for a device and a value of the attribute for a user of the device. A particular group of user types to which the user belongs is determined based on the value of the attribute for the user. A context label based on the context data and the particular group is determined to be sent. Some techniques include determining a value of an attribute and context data based on context measurements. First data that indicates the context data and the value of the attribute is determined to be sent. A context label based on the context data and the value of the attribute is received.

Abstract: An approach is provided for initiating a device action in response to determining context information associated with a device, a user of the device, or a combination thereof. Activity information on at least one of the device and one or more other devices is monitored by a context processing platform. The context processing platform defines a context based on the activity information. An action is then executed by the device or the one or more other devices based on the determined context.

Abstract: An approach is provided for training a device to associate user, object or device context information with a user defined context. The context information platform receives recorded context information from a device. The associated recorded context information is then associated with the context to enable training of a context model associated with the context.

Abstract: An approach is provided for characterizing user behavior patterns. The behavior pattern platform receives a plurality of context records from a device. Next, the behavior pattern platform places one or more contexts from the context records. Then, the behavior pattern platform places the contexts into one or more context groups. Then, the behavior pattern platform receives interaction data from the device, associates the context groups with the interaction data, and determines a behavior pattern based, at least in part, on the association of the context groups and the interaction data.

Abstract: Various methods for modeling personalized contexts are provided. One example method includes accessing a context data set comprised of a plurality of context records. The context records may include a number of contextual feature-value pairs. The example method may also include generating at least one grouping of contextual feature-value pairs based on a co-occurrence of the contextual feature-value pairs in context records, and defining at least one user context based on the at least one grouping of contextual feature-value pairs. Similar and related example methods and example apparatuses are also provided.