Systems, methods, and computer-readable media are disclosed for caching selected data for use in real-time content selection. In one embodiment, an example method may include determining a first set of user identifiers for users that are eligible to be presented with content associated with a first product identifier, the first set having a first number of user identifiers, determining a second set of user identifiers for users that are eligible to be presented with content associated with a second product identifier, the second set having a second number of user identifiers, and determining that the first number is greater than the second number. Example methods may include determining first content associated with the first product identifier, determining first product information associated with the first product identifier, and caching the first content and the first product information at a server instead of second content associated with the second product identifier.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform determining a respective interaction metric between at least two respective keywords of a plurality of keywords; constructing, using each respective interaction metric, as determined, a graph comprising a plurality of nodes connected by at least one edge; identifying one or more clusters of nodes using the graph; optimizing each cluster of nodes of the one or more clusters of nodes; and facilitating altering a graphical user interface (GUI) of an electronic device based upon a cluster of nodes of the one or more clusters of nodes, as identified and optimized. Other embodiments are disclosed herein.

Systems and methods described herein may generate campaigns and efficiently calculate bids for placement of campaign data into Internet data. Embodiments may calculate context scores for campaign data based on campaign terms and beacon terms. The context scores may be used to identify Internet content that has a high page score. If the page score of particular Internet content exceeds a predetermined threshold, the system may place a bid for a campaign based on disclosed algorithms taking as inputs performance scores, context scores, page scores, campaign budgets, and other parameters. The systems therefore are capable of quickly and effectively calculating optimal bids to place for a particular campaign given parameters disclosed herein.

Herein are techniques for content delivery pacing based on multidimensional forecasting. In an embodiment, a computer receives, for a content delivery campaign, targeting criteria and a resource usage limit of a limited resource. Entities that match the targeting criteria are identified for which content of the delivery campaign may have increased relevance. For each matching entity, a forecast of requests that might originate from the entity during each of a series of time intervals is generated to predict opportunities to deliver the content of the campaign. The forecasts of the matching entities can be combined to generate a combined forecast of requests for the targeting criteria. The computer generates, based on the combined forecast and the resource usage limit for the content delivery campaign, and stores for future use a fulfilment schedule that specifies amounts of requests to fulfill during the series of time intervals.

Aspects of the subject disclosure may include, for example, receiving from a campaign manager device information defining a line item in an online advertising system, including receiving information defining constraints for the line item. The subject disclosure may further include collecting browsing history information for targetable users matching the constraints for the line item, generating a machine learning model to rank the targetable users and building a new segment based on users ranked by the model. The subject disclosure may further include providing, to the campaign manager device, a recommendation to add the new segment to the line item, receiving from the campaign manager device an indication to attach the new segment to the line item, and subsequently, providing advertisement content to targeted users according to the line item including the new segment. Other embodiments are disclosed.

An improved architecture including system and methods for online advertising placement that provide possibly defaulting advertisement tags the opportunity to serve an advertisement ahead of a lower value tag that is guaranteed to fill, resulting in higher CPMs (i.e., Cost Per Mille) for web publishers. The system and methods are configured to deterministically render an advertisement impression from a list of possibly defaulting advertisements in a JavaScript-enabled web browser. The knowledge of the complete outcome of such an “ad chain” at render-time significantly reduces complexity and latency in the supporting ad server. The system and method centers around a novel JavaScript approach to detect when an advertisement has been loaded but not defaulted. Additionally, the system and methods integrate the network and RTB demand channels by looking at all demand sources simultaneously and selecting the buyer from within the user's browser, and address predictive pricing to further enhance the online advertising placement process.

Methods and systems are described for providing advertising services to devices with a customized adaptive user experience based on adaptive algorithms. In one embodiment, a system includes a storage medium to store one or more software programs and an adaptive decision unit coupled to the storage medium. The adaptive decision unit includes or is coupled to processing logic that is configured to execute instructions of at least one adaptive decision algorithm to obtain data for different variables including at least two of an advertisement (ad) engagement history for a user, application (app) engagement history for the user, and in-app purchase engagement history for the user when making an ad selection decision. The at least one adaptive decision algorithm determines an ad selection decision for at least one relevant ad or ad content served in an engaging manner to a device of the user.

A request for a page is received. The page is provided at least in part by determining dynamically a content element for the page. An auction to determine a winning bid is conducted prior to providing the page in response to the request and the content element determined based at least in part on the auction winner.

A delayed grouping (batch) processing of previous campaign delivery pacing decisions and corresponding outcomes (deliveries) is used to configure a new auction experiment iteration. In the new iteration, a campaign that was previously over-delivered is classified as either (a) over-delivered due to incorrect pacing or (b) over-delivered due to auction experiment design. After the delayed processing, the new auction experiment iteration is conducted with a mitigating action taken on the previously over-delivered campaign if the campaign is classified as (b) over-delivered due to auction experiment design. For example, the mitigating action can include removing the campaign from a subsequent iteration of the experiment, or the experiment can be redesigned. By doing so, the over-delivery caused by the campaign due to the auction experiment design is avoided when performing the new auction experiment iteration.

Techniques for manipulating video livestream background images are disclosed. A short-form video, such as a livestream video or livestream replay video, can be analyzed for context. Computer-implemented techniques may be used for performing entity detection, and can also detect a change in subject based on speech and/or actions of a host individual. The subject can include a particular product. The detecting a change in subject can include detecting a foreground object and identifying the foreground object as a product. The identification of the foreground object as a product can include performing optical character recognition on text imprinted on a foreground object. The identification of the foreground object as a product can include image recognition techniques. The identification of the foreground object as a product can include scanning of an optical code such as a barcode that is imprinted on the product.