A system and method for scheduling at least one advertisement for presentation in connection with at least one media program is presented. In one embodiment, the method comprises determining a measure of engagement of at least a portion of the at least one media program, selecting the at least one advertisement from the plurality of advertisements based at least in part upon the determined measure of engagement of the at least a portion of the media program, and scheduling the selected advertisement for presentation in connection with the selected at least one advertisement.

Systems, devices, media, instructions, and methods are provided for presentation of media collections with automated interactive advertising. In one embodiment, a server system merges advertising data with interaction elements to create an advertising element, and communicates the advertising element to a client device with other pieces of content. In another embodiment, advertising data is received at a client device and merged at the client device with interaction elements to generate an advertising element. The advertising element is then displayed between content elements that are part of one or more content collections. In various embodiments, interaction data recorded at the device is used to manage the presentation of future advertising elements.

A method and system provide the ability to manage entities of a marketing domain model in a multi-state workflow. Multiple entities are acquired in a content hub. Each entity is a set of data that belongs together as one and includes properties that describe entity details. Relations are created between the multiple entities to give meaning to the marketing domain model. A solution graph is generated that represents all of the multiple entities (nodes) and relations (edges). Inside the solution graph, a state workflow can be created for each node. Nodes can be linked to a state and there are transitions between the states. Multiple non-linear state workflows can be orchestrated by an overall waterfall-based workflow (that is linear and time duration based. A graphical user interface enables management of and renders a representation of the multiple entities, the solution graph, and the workflows.

In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system using predictive models derived from tiered feature data.

A system and method are provided for a pedestrian thoroughfare portable media projection kiosk. The method provides a portable kiosk chassis located in a pedestrian thoroughfare. A media projection subsystem attached to the kiosk projects a media message and creates a media enablement signal. The media message may be a visual display, an audio broadcast, or both. The method verifies that media has been projected in response to receiving a media enablement signal. Verification information is supplied that includes the media enablement signal, and the verification information is communicated to a server. The verification information may be communicated using a cellular telephone, an IEEE 802.11 Wireless Local Area Network (WLAN) device, an IEEE 802.15 Wireless Personal Area Network (WPAN) device, or combinations thereof. Alternatively, or in addition, the kiosk may provide a publically accessible cellular telephone booster, a WLAN access point (AP), WPAN AP, or combinations thereof.

Systems and methods for managing feedback for a multi-ARM bandit algorithm are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include sending, to a device, a creative configured for display on the device. The operations may include receiving, from the device, feedback data associated with the creative, wherein the feedback data indicates a degree of user interaction with the creative. The operations may include checking the feedback data for a baked time associated with the creative. The operations may include sending the feedback data to a node based on the baked time. The operations may include updating, by the node, a model associated with the feedback data.

A system for video generation having a computer, a database in data communication with the computer, a plurality of blueprints and a user device in data communication with the computer. The computer receives at least one asset and a selection of a blueprint from the user device. The computer generates a video based on the at least one asset and the blueprint associated with the selection. The computer is in data communication with at least one advertising platform which receives the video and displays the video. The advertising platform generates performance data based on the display of the video. The computer generates a second video based on the at least one asset, the blueprint associated with the selection, and the performance data.

Exemplary embodiments include a reinforcement learning model configured at a given point in time to receive digital data about a state of a user at the given point in time, receive digital data about an environment at the given point in time, receive digital data about a campaign at the given point in time, optimize total expected future number of positive rewards at the given point in time, and to execute an action at the given point in time. The state of the user at the given point in time may be a number of communications the user has received in a particular time period, a time since a last communication, the user's past behavior, and/or the user's engagement score from a predictive model to engage with a communication.

[Problem to be solved] An object of the present invention is to provide a method for improving an advertising effect, particularly in a television commercial. [Solution] According to an embodiment of the present invention, the disclosed method is a method for providing an advertisement for a television program, which determines an advertisement-related product related to a product to be advertised, refers supply prediction information of the advertisement-related product, and determines the time for placing the TV program advertisement based on the supply prediction information.

Systems and methods for scoring promotions are provided. A set of training offers are received, which include combinations of variable values. These combinations of variable values are converted into a vector value. The offers are paired and the vectors subtracted from one another, resulting in a pair vector. Metrics for the success of offers is collected, and are subtracted from one another for the paired offers to generate a raw score. This raw score is then normalized using the pair vector. The normalized scores are utilized to generate a model for the impact any variable value has on offer success, which may then be applied, using linear regression, to new offers to generate an expected level of success. The new scored offers are ranked and the top-ranked offers are selected for inclusion in a promotional campaign.