Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for adaptively generating an electronic bid value for a tail digital content object.

Disclosed herein is a web user experience improvement for digital magazines. A digital magazine viewing platform is integrated with a digital magazine publishing platform including features that leverage the integration including user interface arrangement based on viewing habits and ripped content that is insertable into draft digital magazine documents. In some embodiments, a machine learning model categorizes magazine styles and present publishing features based on those magazines viewed or subscribed to by a given user.

One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with a sparse set of field weights associated with feature fields associated with features of the plurality of sets of auction information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. Positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon field weights, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the positive signal probabilities.

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for selecting ads for a video. The program and method provide for receiving a request for an ad to insert into a video playing on a client device, the request including a first content identifier that identifies a first type of content included in the video; determining a set of content identifiers associated with the first content identifier, the set of content identifiers identifying second types of content to filter with respect to providing the ad in response to the request; selecting an ad from among plural ads, by filtering ads tagged with a second content identifier included in the set of content identifiers; and providing the selected ad as a response to the request.

One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

Enabling advertisers using a computer network such as the Internet and a match engine to submit their offerings to product, service, benefit seeking entities. In some embodiments, a database having accounts for the providers is made available. Accounts contain contact and billing information for an advertiser; and at least one offering having at least a description, a criteria set comprising one or more criterion factors, and a bid amount. An advertiser influences a position of an offering in the advertiser's account by first selecting offering relevant criteria. The advertiser enters the criteria and the description into a listing; influencing at least in part the position for the listing within a results page through an online bidding process. This results page is generated in response to a seeking entity query of the match engine. Pay for performance demographic, geographic, psychographic criteria/characteristics targeted directly advertising (frictionless advertising) is enabled.

A method of providing relevant messages to an automotive virtual assistant is provided. The method includes receiving a spoken utterance and corresponding first geolocation information detected by a subsystem of a first automobile, parsing the spoken utterance to determine concepts and storing the concepts in a concept database indexed by the corresponding first geolocation information. The method further includes receiving second geolocation information detected by a subsystem of a second automobile, searching the concept database for an index based on the second geolocation information to find a stored concept of the stored concepts, searching a natural language expression database using the stored concept as an index to find an assistive natural language expression, wherein the assistive natural language expression includes a constituent part, and sending the assistive natural language expression to the second automobile with the stored concept in place of the constituent part.

A system that handles delivery of a service through a client device or a secondary device paired with the client device, includes an interactive service provider and the client device. The interactive service provider inserts at least one of digital watermarks, fingerprints, and trigger identifiers at event opportunities in media content. The client device detects at least one of the inserted digital watermarks, the digital fingerprints, and the inserted trigger identifiers in the media content. The client device further renders overlay graphics on the media content and activates at least one of input devices in vicinity of the client device or the rendered overlay graphics. The client device receives trigger responses over an activated overlay graphic, via the activated input devices. The client device further displays an interactive view on the client device, to enable delivery of services in response to the received trigger responses.

A method including receiving a digital promotion payload from a brand manufacturer for at least one branded consumer packaged good, the digital promotion payload including a digital promotion value associated with the branded consumer packaged good, is provided. The method includes receiving a bid request to the digital promotion engine, providing a bid response to the bid request, the bid response including the digital promotion payload, and receiving, from the supply side platform, a confirmation that the bid response has been selected from one or more bids from different digital advertising entities. The method includes providing a command to the supply side platform to deliver the digital promotion payload to a mobile device accessing a resource from the mobile display publisher, and loading the digital promotion value to a frequent shopper identification in response to a consumer interaction with the digital promotion payload detected from the mobile device.