Abstract: A method of gathering information relevant to the interests of a user includes receiving activity history associated with the user from a network data source, where the activity history includes a description of a user action initiated by the user, generating a first metadata item based upon the activity history; and storing the first metadata item as user profile information associated with the user in a user profile database. Generating the first metadata item may include extracting text from the activity history and generating the first metadata item based upon the text. The activity history may include a description of a user action.

Abstract: Embodiments are directed towards providing related keywords determined to be relevant to a search term using life-streaming data to identify the related keywords. Relevancy is determined by performing the related keyword analysis when life-stream services have been updated. Related keywords may be identified for a given search term using a combination of an aggregated life-stream analysis, a user profile based analysis, and/or a community based life-stream analysis. Each of these mechanisms identify a subset of updated life-streams or profiles to be analyzed to identify keywords that co-occur within each life-steam along with the given search term. The resulting co-occurring keywords may then be ordered based on a frequency of occurrence for the subset of life-streams. A weighted combination of the identified related keywords may be used to display related keywords to a user for use in modifying a search query.

Abstract: Embodiments are directed towards determining frequencies for adaptively crawling user activity feeds, by taking into account user activity patterns, as well as a type or nature of a service to be crawled. A statistical model of activity patterns is generated for each user and user activity source based on various parameters. The activity patterns may be weighted to account for variation in relevancy of user content. Various measures are generated that are adaptive by being updated at a rate determined sufficient to detect user activity pattern changes. The statistical model produces clusters of users for each service. A crawling frequency may be assigned to the various clusters such that different users for a given service may have content crawled at different frequencies.

Abstract: Embodiments are directed towards providing related keywords determined to be relevant to a search term using life-streaming data to identify the related keywords. Relevancy is determined by performing the related keyword analysis when life-stream services have been updated. Related keywords may be identified for a given search term using a combination of an aggregated life-stream analysis, a user profile based analysis, and/or a community based life-stream analysis. Each of these mechanisms identify a subset of updated life-streams or profiles to be analyzed to identify keywords that co-occur within each life-steam along with the given search term. The resulting co-occurring keywords may then be ordered based on a frequency of occurrence for the subset of life-streams. A weighted combination of the identified related keywords may be used to display related keywords to a user for use in modifying a search query.

Abstract: Embodiments are directed towards providing related keywords determined to be relevant to a search term using life-streaming data to identify the related keywords. Relevancy is determined by performing the related keyword analysis when life-stream services have been updated. Related keywords may be identified for a given search term using a combination of an aggregated life-stream analysis, a user profile based analysis, and/or a community based life-stream analysis. Each of these mechanisms identify a subset of updated life-streams or profiles to be analyzed to identify keywords that co-occur within each life-steam along with the given search term. The resulting co-occurring keywords may then be ordered based on a frequency of occurrence for the subset of life-streams. A weighted combination of the identified related keywords may be used to display related keywords to a user for use in modifying a search query.

Abstract: Embodiments are directed towards providing related keywords determined to be relevant to a search term using life-streaming data to identify the related keywords. Relevancy is determined by performing the related keyword analysis when life-stream services have been updated. Related keywords may be identified for a given search term using a combination of an aggregated life-stream analysis, a user profile based analysis, and/or a community based life-stream analysis. Each of these mechanisms identify a subset of updated life-streams or profiles to be analyzed to identify keywords that co-occur within each life-steam along with the given search term. The resulting co-occurring keywords may then be ordered based on a frequency of occurrence for the subset of life-streams. A weighted combination of the identified related keywords may be used to display related keywords to a user for use in modifying a search query.

Abstract: Embodiments are directed towards determining frequencies for adaptively crawling user activity feeds, by taking into account user activity patterns, as well as a type or nature of a service to be crawled. A statistical model of activity patterns is generated for each user and user activity source based on various parameters. The activity patterns may be weighted to account for variation in relevancy of user content. Various measures are generated that are adaptive by being updated at a rate determined sufficient to detect user activity pattern changes. The statistical model produces clusters of users for each service. A crawling frequency may be assigned to the various clusters such that different users for a given service may have content crawled at different frequencies.

Abstract: A method of gathering information relevant to the interests of a user includes receiving activity history associated with the user from a network data source, where the activity history includes a description of a user action initiated by the user, generating a first metadata item based upon the activity history; and storing the first metadata item as user profile information associated with the user in a user profile database. Generating the first metadata item may include extracting text from the activity history and generating the first metadata item based upon the text. The activity history may include a description of a user action.