To propose efficient advertisement allocation to particular targets. An advertising material allocation system according to one aspect of the present disclosure includes: an acquisition unit configured to acquire an acquired target rating point (TRP) of each advertisement frame and restriction information about allocation of each advertisement frame; and a control unit configured to decide an advertising material for a particular target to be allocated to each advertisement frame, based on the acquired TRP and the restriction information.

Some implementations relate to methods, systems, and computer-readable media for digital advertising within a first virtual experience provided at a virtual experience platform. A virtual user engagement portal is provided within the first virtual experience that includes a portal member configured to transport an avatar to a second virtual experience. The second virtual experience may be associated with at least one media item, and may include a plurality of virtual items, activities, and other virtual content associated with the at least one media item. A return portal may be provided within the second virtual experience to return an avatar to the first virtual experience. User engagement metrics and/or advertising metrics may be aggregated and/or computed based on avatar interactions within the second virtual experience.

An advocacy system uses trained machine learning models to create messages that are sent to advocates or policymakers to achieve desired outcomes for an organization. Desired outcomes can include, for example: an advocate sending a message to a policymaker or legislative representative advocating in favor or the organization’s position on an issue; a policymaker acting or voting in favor of the organization’s position on an issue; or an advocate making a financial contribution to the organization. The machine learning models can be configured to select possible message characteristics or features that the system will include/use in creating/sending messages to/for individual senders and recipients. The machine learning models can be trained based on message characteristics, personal profile characteristics of senders/recipients, and outcomes from previously sent messages. Personal profile characteristics of senders/recipients can indicate correlations between certain message characteristics and certain outcomes of sending messages.

A method includes causing a computer of a communication terminal to perform a process including acquiring, in response to receipt of a beacon signal from a vending machine, identification information, type information of a plurality of drinks, and stock information of the plurality of drinks from the vending machine by using short-range wireless communication; transmitting a user ID of a user of the communication terminal to a personal information server; acquiring biological information that includes a body temperature or a body water percentage of the user corresponding to the user ID from the personal information server; generating, based on the identification information, the type information, the stock information, and the biological information, a push notification screen that recommends at least one drink suitable for hydrating the user to prevent heatstroke; and displaying the push notification screen on a display of the communication terminal.

The subject matter of this specification can be embodied in, among other things, a method that receives at a server a first request to rebroadcast a first sponsored content item that was displayed on the social network to the first user to a second user. A second request to display at least one sponsored content item to the second user when the second user accesses an electronic document associated with the social network is received at the server. The first sponsored content item is retrieved for rebroadcast from one or more sponsored content items indicated for rebroadcast to the second user. The first sponsored content item is output to the second user of the social network in response to the second request to display and without requiring the second user to select the first sponsored content item for display.

An online system receives offline conversion data in real-time from a third party system regarding an offline user, the offline conversion data indicating an action performed by the offline user and identifying information for the offline user. The online system identifies a local user matching the offline user based on the identifying information for the offline user. The online system stores the offline conversion for the identified local user. The online system determines an attribution to a sponsored content item for the offline conversion. The online system presents updated information regarding the offline conversion to the third party system in real-time.

Systems, methods, devices, and non-transitory media of the various embodiments may include a demand-side platform (DSP) supporting digital asset provisioning. Various embodiments may provide DSP enabled methods for rewarding end users for engaging with and/or supplying data to advertisers. Various embodiments may provide a DSP that supports methods for rewarding end users with digital assets for engaging with and/or supplying data to advertisers. Various embodiments may enable advertisers to structure advertising campaigns and allocate digital assets for rewards for user engagement with advertisements in the advertising campaign via a DSP. Various embodiments may enable advertisers to verify that user interactions, such as clicks, downloads, views, etc., were actually performed by a human user.

A request for a page is received. A processor is used to provide an optimized version of the page at least in part by determining dynamically an optimized content element for the page based at least in part on a user attribute associated with the request and content performance data generated based at least in part on testing of alternative versions of the page with respect to other users having the user attribute. The provided optimized version of the page is selected from the alternative versions of the page based on the content performance data indicating that the selected version of the page is optimized for the other users having the user attribute.

A merchant may operate a retail store that users are able to visit in person in order to view and purchase products. When a user visits the store, the user might not know where a desired product (“target product”) is located. Computer technology may help direct the user to the target product in real-time. In some embodiments, a model of passable areas and the location of products in the retail store may be determined, e.g. by a merchant device. In some embodiments, when the user visits the retail store, a computer generates a product recommendation, e.g. based on user-specific information, and a route in the retail store is determined for the user. In some embodiments, the route in the retail store may be determined using the model based on the target product, the user's location in the store, and one or more recommended products.

Embodiments of the present disclosure provide mechanisms for selection of a user survey data object from a set of user data objects, and processing of survey engagement data associated with a selected user survey data object. The user survey data object selected is appropriate for providing associated with a particular user data object, and the survey engagement data received associated therewith enables programmatic selection and use of particular ranking model(s) for use in generating and providing an output ranked item data object set. Example embodiments utilize selected ranking model(s) of a set of ranking models to programmatically generate and output an output ranked item data object set for a particular user profile.