Abstract:
Systems and methods for identifying users according to their activity are disclosed. The identification of a user includes accessing a user activity log having a plurality of identifiers and corresponding activity information for each identifier, determining identifiers having correlating activity information, and assigning identifiers having correlating activity information to a common user.
Abstract:
Method, system, and programs for providing content recommendation are disclosed. A first set of candidate content items may be generated based on a user profile, and a second set of candidate items may be generated based on the likelihood that the user will click a corresponding candidate content item in the second set. The candidate content items in the first and second sets may be ranked together using a learning model and presented to the user as content recommendations based on their rankings. The likelihood that the user will click a given candidate content item in the second set may be estimated based on similarities between the given content item and content items related to the given content item. Such a similarity may be computed based on activities performed by users who have viewed both the given content item and a related content item.
Abstract:
The present teaching relates to online user profiling. In one example, content associated with a first user of a social media network is obtained. From the content associated with the first user, a first link to a first piece of content is identified. A second user of the social media network associated with the first user is determined in the context of the social media network. From content associated with the second user of the social media network, a second link to a second piece of content is identified. The first and second pieces of content are retrieved based on the first and second links, respectively. User profile of the first user is generated based, at least in part, on the first and second pieces of content.