Abstract: An analysis application utilizes interactivity signals to generate relationships and promote content. One or more interactivity applications, such as a social networking application, are queried to retrieve interactivity signals. Interactivity signals include an interaction pattern that indicates a relationship between a user and relations of the user. A relationship graph is constructed based on the interactivity signals. Content associated with a user is promoted based on the relationship graph. A weight of the interactivity signals is adjusted to improve a ranking of the relationship graph and a ranking of the content.

Abstract: An analysis application utilizes interactivity signals to generate relationships and promote content. One or more interactivity applications, such as a social networking application, are queried to retrieve interactivity signals. Interactivity signals include an interaction pattern that indicates a relationship between a user and relations of the user. A relationship graph is constructed based on the interactivity signals. Content associated with a user is promoted based on the relationship graph. A weight of the interactivity signals is adjusted to improve a ranking of the relationship graph and a ranking of the content.

Abstract: Collected usage information relating to interaction with content is integrated with operation of a system, such as a content management system. The usage information is collected in response to occurrences of usage events. A user may create, configure and define the usage events according to their needs. The user may also configure/define different values that are associated with the different events. For example, a user may specify: when to count a view (e.g. unique user view or each view); what period of time to collect counts; aggregation of counts (e.g. tenant, site, collection); a weighting for the event; a trim value; when to provide the parameter to a search index of the system such that the value is considered by the search engine of the system; whether or not a parameter is configurable by other users, and the like.

Abstract: Collected usage information relating to interaction with content is integrated with operation of a system, such as a content management system. The usage information is collected in response to occurrences of usage events. A user may create, configure and define the usage events according to their needs. The user may also configure/define different values that are associated with the different events. For example, a user may specify: when to count a view (e.g. unique user view or each view); what period of time to collect counts; aggregation of counts (e.g. tenant, site, collection); a weighting for the event; a trim value; when to provide the parameter to a search index of the system such that the value is considered by the search engine of the system; whether or not a parameter is configurable by other users, and the like.

Abstract: Collected usage information relating to interaction with content is integrated with operation of a system, such as a content management system. The usage information is collected in response to occurrences of usage events. A user may create, configure and define the usage events according to their needs. The user may also configure/define different values that are associated with the different events. For example, a user may specify: when to count a view (e.g. unique user view or each view); what period of time to collect counts; aggregation of counts (e.g. tenant, site, collection); a weighting for the event; a trim value; when to provide the parameter to a search index of the system such that the value is considered by the search engine of the system; whether or not a parameter is configurable by other users, and the like.

Abstract: A personalized view of insights into social activity surrounding a content item is provided that may indicate to a user why a given piece of content may be interesting to him/her. A user's activities, as well as the activities of others (e.g., colleagues of the user) may be shown, including such items as a total number of views, comments, followers, and likes associated with the content item. The insights view may be personalized based on the user's relationships with other users in association with the content item. The user may learn through the personalized view who among his/her relationships have commented, edited, shared, or liked the content item, as well as, how many of the user's colleagues have viewed the content item. In addition, through a search function that may be applied to the view content, a user may learn about related content items based on the social insights.

Abstract: A computer-implemented method that monitors the activity of different nodes within a system as well as crowd sourcing activity. The computer-implemented method determines that a first node formed a relationship with a second node, generates an edge based on the relationship between the first node and the second node, stores the edge in a graph index and assigns a privacy setting to the edge based on the relationship between the first and second nodes.

Abstract: A personalized campaign engine is provided. The personalized campaign engine selects a user as a candidate receiver of a campaign message. A personalized campaign message is generated for the user that comprises content items that are relevant to the user based on identified relationships between the user and content items and relationships between the user and other individuals. Relationships between the user and content items and relationships between the user and other individuals are identified by interrogating disparate repositories of information for organizational relationship data and activity data associated with the user. The personalized campaign engine customizes the campaign message for the user by using known information about the user to select a certain template, medium, and delivery strategy.

Abstract: A computer-implemented method that monitors the activity of different nodes within a system as well as crowd sourcing activity. The computer-implemented method determines that a first node formed a relationship with a second node, generates an edge based on the relationship between the first node and the second node, stores the edge in a graph index and assigns a privacy setting to the edge based on the relationship between the first and second nodes.

Abstract: A personalized view of insights into social activity surrounding a content item is provided that may indicate to a user why a given piece of content may be interesting to him/her. A user's activities, as well as the activities of others (e.g., colleagues of the user) may be shown, including such items as a total number of views, comments, followers, and likes associated with the content item. The insights view may be personalized based on the user's relationships with other users in association with the content item. The user may learn through the personalized view who among his/her relationships have commented, edited, shared, or liked the content item, as well as, how many of the user's colleagues have viewed the content item. In addition, through a search function that may be applied to the view content, a user may learn about related content items based on the social insights.

Abstract: An analysis application utilizes interactivity signals to generate relationships and promote content. One or more interactivity applications, such as a social networking application, are queried to retrieve interactivity signals. Interactivity signals include an interaction pattern that indicates a relationship between a user and relations of the user. A relationship graph is constructed based on the interactivity signals. Content associated with a user is promoted based on the relationship graph. A weight of the interactivity signals is adjusted to improve a ranking of the relationship graph and a ranking of the content.

Abstract: Collected usage information relating to interaction with content is integrated with operation of a system, such as a content management system. The usage information is collected in response to occurrences of usage events. A user may create, configure and define the usage events according to their needs. The user may also configure/define different values that are associated with the different events. For example, a user may specify: when to count a view (e.g. unique user view or each view); what period of time to collect counts; aggregation of counts (e.g. tenant, site, collection); a weighting for the event; a trim value; when to provide the parameter to a search index of the system such that the value is considered by the search engine of the system; whether or not a parameter is configurable by other users, and the like.

Abstract: A search driven user interface (UI) is used for navigating content and usage analytics within a system. The system is configurable such that users of the system may configure the usage events that are used to collect the usage information and configure the display of the content and the usage analytics both in a navigation mode and in an analytics mode. For example, a user may configure an overview page on a product catalog to show main product categories and popular products when in navigation mode and when an analytics mode is selected, the page shows different usage analytics for the main product categories (e.g. view counts, purchase event counts, popularity, demographic information, and the like). A user may switch between the navigation mode and the analytics mode while exploring the content of the data selection.