Systems and methods for identifying an audience set are disclosed. A request receive identifying a future time period and item class is received and a conversion value for each of a set of user identifiers is generated by implementing a trained statistical machine learning model using historical transaction data. A first subset of user identifiers and a second subset of user identifiers are identified based on threshold values of the conversion value. The subsets are each associated with targeted advertisement types corresponding to a particular level of specificity associated with the requested item class. The first targeted advertisement type is presented to user devices associated with the first subset of user identifiers and the second targeted advertisement type is presented to user devices associated with the second subset of user identifiers.

A method includes receiving a request and request data associated with a user from a web server and analyzing the request data to identify one or more data gaps associated with the request. One or more third-party services are called to fill at least a portion of the one or more data gaps. A question set is prepared based on determining that the one or more data gaps remain at least partially unfilled. The question set is selected by a machine-learning component trained to adapt a sequence and content of the question set over a plurality of interactions with a plurality of users. The question set is transmitted to the web server for presentation to the user. Data exchanges can be authenticated using tokens.

A method of recommending content, an electronic device, and a computer-readable storage medium, relate to a field of artificial intelligence, especially a field of intelligent recommendation. The method includes: determining a target content from candidate contents based on a query of a user, the candidate contents being determined based on a content-related user attention; determining an estimated user cost for acquiring the target content, based on a historical click-through rate for the target content, a historical conversion rate for the target content, and a historical user cost for acquiring the target content; determining one or more recommendation scores for the target content based on the estimated user cost; and recommending a content to the user based on the one or more recommendation scores.

Methods and systems for determining reach information. Watching habits of viewers in a designated market can be analyzed using machine learning algorithms to obtain campaign spot plans. The campaign spot plans can be applied to single viewer data to calculate campaign spot plan Television Average Ratings Points (TARP) pattern information. The TARP pattern information can be translated into reach information determining how many people were uniquely exposed to each campaign spot.

This disclosure generally covers systems and methods that determine demographic labels for a user or a group of users by using digital inputs within a predictive model for demographic classification. In particular, the disclosed systems and methods use a unique combination of classification algorithms to determine demographic labels for users as a potential audience of digital content items. When applying the combination of classification algorithms, the disclosed systems and methods use a first classification algorithm to determine user-level-latent features for each user within a group of users based on demographic-label statistics associated with particular digital content items. The disclosed systems and methods then use the user-level-latent features and session-level features (from sessions of each user consuming the digital content items) as inputs in a second classification algorithm to determine a demographic label for each user within the group of users.

The present disclosure presents systems and related methods for creating real-time predictions. One such method comprises receiving, by a computing device, a first set of data and a second set of data, wherein the first set of data comprises a plurality of items available from a first source for a first set of users and the second set of data comprises transaction purchase data for a second set of users that have reward accounts, utilizing a predictive data model that determines a propensity score for a user from only behavior data that is not attributed to the user; receiving a third set of data from a third source comprising social media channel data for a third set of users; and updating the predictive data model to determine the propensity score for the user based at least in part on the third set of data.

Systems and methods for automatically soliciting the purchase of a first or second machine-related resource in a forward market, wherein the first resource and the second resource are distinct instances of the same type of resource, are described. A sample system may include a fleet of machines, each having a resource requirement comprising at least two of: a compute resource, a spectrum resource, or a network bandwidth resource. The system may include an circuits to aggregate data corresponding to the machine-related resources from at least a behavioral data source, to determine a substitution cost of a second resource; to determine a machine-related resource acquisition value; and to automatically solicit a purchase, in a forward market, of one of the first resource or the second resource in response to the determined substitution cost of the second resource.

Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, for providing content. A method includes receiving a request for an advertisement to be displayed in a slot associated with a third-party content site; identifying a relevant advertisement to be provided in the slot; determining information to be included in an annotation associated with the advertisement; the annotation including customized information to be presented along with the advertisement; providing the advertisement responsive to the request including providing the annotation along with a control for re-publishing the advertisement along with the relevant advertisement; receiving user input selecting the control and designating the advertisement for re-publishing to a group, the group being designated by the user; and targeting additional content to the group based on the received user input.

The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which may be used in conjunction with a campaign for distributing content to users of the social network. Among other things, embodiments of the present disclosure provide a number of advantages over conventional systems for content distribution, including a simplified targeting process and increased reach (i.e. distribution) for content providers among users of a social network, as well as improving the utilization of an inventory of content and higher and more efficient engagement with such content by users of the social network.

In one aspect, an example method includes (i) determining an estimated number of replacement advertisement segment viewings remaining before an end date of a first advertising campaign; (ii) determining a number of impressions remaining for the first advertising campaign in order to reach a guaranteed total; (iii) determining, using the estimated number of replacement advertisement segment viewings and the number of impressions remaining, a first value of serving a first replacement advertising segment corresponding to the first advertising campaign to a content-presentation device; (iv) determining a second value of serving a second replacement advertisement segment corresponding to a second advertising campaign to the content presentation device; (v) selecting the first replacement advertisement segment rather than the second replacement advertisement segment based on the first value being greater than the second value; and (vi) causing the first replacement advertisement segment to be transmitted to the content-presentation device.