Abstract:
This disclosure relates to personalizing user interface (UI) elements of online content (a website, a mobile application, etc.) presented on a user device. The UI personalization technique may include, for a current online session, processing user-related data and context-related data to determine the UI element(s) and their attribute value(s) to be used for presentation of the online content during the current session. The user-related data include information regarding the user/user device, and the context data may include details about the online content (content type/topic, default UI attribute values, etc.) being accessed in the given online session. The user data and the context data may be processed based on modeling data related to users and their interaction with the UI of online content from different publishers and/or advertisers. Based on such processing, personalized UI element(s) and attribute values are determined and the online content is presented with the personalized UI.
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:
Systems and methods for providing a unified targeting solution are disclosed. The system obtains user data for each user in a user group from a database stored in the non-transitory storage medium. The database is organized on a user by user basis and includes signals from a plurality of sources. The system receives an input from an advertiser including a marketing intention. The system includes features extracted from the user data and the input. The system obtains a score for each user based on the extracted features. The system selects users from the user group based on the obtained scores.