An electronic device includes a communicator; at least one memory storing information associated with a neural network model for predicting a target device for each time zone from a plurality of time zones for an advertising content; and at least one processor configured to: acquire, from a plurality of external devices, data corresponding to a use history of a user; acquire, based on the data corresponding to the use history, a plurality of feature values corresponding to a respective external device from the plurality of external devices by allocation of each feature value of the plurality of feature values to a respective time zone from the plurality of time zones, input the plurality of feature values into the neural network model, and acquire score information for a plurality of probabilities that the plurality of external devices are related to a label by allocation of each probability of the plurality of probabilities to a respective time zone from the plurality of time zones, identify, based on the score information, a plurality of target devices for providing the advertising content for each of the plurality of time zones, and control the communicator to transmit information on the advertising content to the plurality of target devices.

The subject personalized consumer advertising/ad placement system provides the ability for advertisers, ad agencies, and any other applicable organization to determine and electronically present their ideal consumer profile and have their advertisement/promotion placed in front of all consumers who match the profile based on the anonymous mining of the consumers actual spending across a broad base of spending categories.

Methods and systems for content management are described. If programming content is distributed to viewers, signals are transmitted with the programming content to indicate a segmentation in the programming content. If a user device sees the signal indicating the advertisement break, the use device may make a request to an advertisement server. The advertisement server may receive the request and determine an identifier associated with the request and an identifier associated with an advertisement break. A binder may bind the identifier associated with the request and the identifier associated with the advertisement break.

Transaction modification based on modeled profiles is described. In an example, transaction data can be received from merchant computing devices associated with merchants associated with a payment processing system. A model can be trained to generate profiles using, as training data, one or more of merchant data, buyer data, or the transaction data. Upon receiving an indication of a particular transaction between a buyer and a merchant, it can be determined that a characteristic of the transaction corresponds to a profile of the generated profiles. Based on the determination that the characteristic corresponds to the profile and the transaction data, a recommendation can be generated for a modification of the transaction to add an item or replace an item.

A method of implementing an influencer-based UX on a consumer device may include storing influencer profile data on a server device, wherein the influencer profile data is associated with an influencer. UX data associated with the influencer profile data may be stored on the server device. The UX data may be associated with the influencer-based UX. At least a portion of the influencer profile data may be transmitted to a consumer device. The portion of the influencer profile data may be displayed on the consumer device with a subscription prompt for the influencer-based UX. Subscription data may be received at the server device and from the consumer device indicating a subscription by the consumer to the influencer-based UX. The UX data may be transmitted to the consumer device and the influencer-based UX associated with the UX data may be implemented on the consumer device.

A system includes a computing platform having processing hardware, and a memory storing software code. The software code is executed to receive digital content indexed to a timeline, receive insertion data identifying a timecode of the timeline, and encode the digital content using the insertion data to provide segmented content having a segment boundary at the timecode, and first and second segments adjoining the segment boundary, wherein the first segment precedes, and the second segment succeeds, the segment boundary. The software code also re-processes the first and second segments to apply a fade-out within or to the first segment and a fade-in within or to the second segment, wherein re-processing the first and second segments provides encoded segments having the segment boundary configured as an insertion point for supplemental content.

Method and computing device for performing dynamic digital signage campaign optimization. Screen data associated to screens controlled by the computing device and requirements of active campaigns are stored at the computing device. The screen data comprise characteristics of the screens and screen activity data defining the activity the screens for the active campaigns. The computing device receives requirements of a candidate campaign and generates a mathematical model based on the requirements of the candidate campaign, the requirements of the active campaigns, and at least some of the screen data. The mathematical model is transmitted to a mathematical solver and a mathematical solution generated by the mathematical solver is received. The computing devices generates configuration data for the candidate campaign based on the mathematical solution. The configuration data define a configuration for displaying a content of the candidate campaign on selected screens among the screens controlled by the computing device.

An autonomous vehicle including a network of one or more sensors positioned on a body of the autonomous vehicle, one or more display devices mounted on one or more exterior sides of the autonomous vehicle, at least one processor, and at least one memory storing instructions is disclosed. The instructions, when executed by the at least one processor, configure the at least one processor to: (i) receive sensor data from the network of one or more sensors; (ii) generate, from the sensor data, metadata associated with a motor vehicle in proximity of the autonomous vehicle; (iii) determine, based at least in part on the metadata, media content for displaying to a user of the motor vehicle; and (iv) initiate display of the media content on the one or more display devices mounted on the one or more exterior sides of the autonomous vehicle.

An audio advertisement delivery method is provided, including: A client (100) sends an advertisement request to a cloud apparatus (20, 90, 110) when playing an audio program (301). The advertisement request includes information about the audio program, an identifier of a target advertisement slot, and a user characteristic. The cloud apparatus (20, 90, 110) determines a vector representation of the target advertisement slot based on the information about the audio program and the identifier of the target advertisement slot (302). The vector representation of the target advertisement slot is used to describe content related in the audio program within a period of time before the target advertisement slot. The cloud apparatus (20, 90, 110) determines, based on the user characteristic and the vector representation of the target advertisement slot, an audio advertisement matching the target advertisement slot (303).

Systems and methods are provided herein for determining personalized timing for generating for display advertisements to users. Rather than an expert determining time segments of a media asset most suitable for presenting advertisements to users, the most suitable time segments in a media asset for presenting advertisements to users may be customized based on a user's profile information and/or the user's level of engagement in a media asset. The media guidance application may parse a media asset into multiple time segments and determine one or more time segments associated with metadata that matches content characteristics preferred by the user. One or more advertisements may be presented to the user in these time segments determined by the media guidance application instead of the time segments determined by the expert.