A method of predicting a person's interests is provided. The method includes receiving geolocation information about a user location, reading, from a database of interpretations, at least one interpretation of an expression made in close proximity to the location, reading, from a database of ad bids, a plurality of ad bids comprising interpretations, comparing the interpretation from the database to the interpretations of the ad bids to select a most valuable ad bid having an interpretation that matches the interpretation of an expression made in close proximity to the location, and presenting an ad associated with the most valuable ad bid, wherein the interpretation is from a natural language expression.

Systems, methods, and computer-readable media are disclosed for systems and methods for automated graphic generation for data sets identified using artificial intelligence. Example methods may include determining a set of user accounts that are similar to a second user account, determining first historical performance data for a first set of digital content associated with the first user account, determining second historical performance data for a second set of digital content associated with the second user account, and generating a first score for the first user account. Example methods may include determining a first number of search queries associated with the first user account, determining a second number of search queries associated with the second user account, generating a second score for the first user account, causing presentation of the first score and the second score at a user interface, and generating an action recommendation for the first user account.

In general, embodiments of the present invention provide systems, methods and computer readable media configured to use a per-set level optimization of the rank order of promotions to be recommended to a consumer. In some embodiments, machine learning is used offline to generate a predictive diversity model that receives one or more similarity rank features associated with a promotion (e.g., category, price band) as input, and produces an output multiplier to be applied to the promotion's respective associated relevance score (e.g., a relevance score representing a prediction of the promotion's conversion rate without diversity features). At run time, per-set optimization of the ordering of a set of promotions is implemented by adjusting the respective associated relevance scores of the promotions using the diversity model and then re-ordering the set of promotions based on their respective adjusted relevance scores.

Systems for optimizing business objectives of e-commerce content can include memory and a processor coupled to the memory. The processor can receive one or more assumptions for multivariate comparison of content. The content can be provided to users of an e-commerce system. The processor can repeatedly generate self-organizing experimental units (SOEUs) based on the one or more assumptions. The processor can inject the SOEUs into the online system to generate quantified inferences about the content. The processor can identify, responsive to injecting the SOEUs, at least one confidence interval within the quantified inferences. The processor can iteratively modify the SOEUs based on the at least one confidence interval to identify at least one causal interaction of the e-commerce content within the system. Other methods and apparatuses are described.

Methods, systems, and media for estimating the causal effect of different content exposure levels are provided.

One or more computing devices, systems, and/or methods for implementing a model for serving exploration traffic are provided. An amount of spend by a content provider to provide content items of the content provider through a content serving platform to client devices of users is determined. A number of exploration impressions of users viewing exploration content items of the content provider over a timespan is determined. A return on exploration impression metric is determined for the content provider based upon a ratio of the amount of spend to the number of exploration impressions. The return on exploration metric is used to rank available exploration content items of content providers for serving exploration traffic.

This disclosure involves allocating content-delivery resources to electronic content-delivery channels based on attribution models accuracy. For instance, a simulation is executed that involves simulating user exposures, times between user exposures, and user responses. The simulation is performed based on parameters associated with simulating user exposures to electronic content-delivery channels and user responses to the user exposures. An accuracy of a channel attribution model when estimating an attribution of an electronic content-delivery channel to a user response is evaluated based on the simulation. A channel attribution model is selected based on the evaluation. An attribution of the electronic content-delivery channel is determined by applying the selected channel attribution model to actual user exposures and actual user responses. This attribution can be used to allocate content-delivery resources to the electronic content-delivery channel in accordance with the selected channel attribution model, and thereby provide interactive content via the electronic content-delivery channel.

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.

Forecasted attributes may be determined for scheduled media items based at least in part on observed characteristics associated with previously presented media items. A division of the scheduled media items into a plurality of media segments may be identified based on the forecasted attributes. A media allocation plan may be determined by solving an optimization problem that includes the media segments and the forecasted attributes.

Systems and methods for modifying a property of a post are provided. A post that includes image content, textual content, and amount content is posted to a platform. Engagement activity data for the post is collected. A matrix that includes scores for properties of the image content, the textual content, audio content, and the amount content is generated. The matrix, the engagement activity data, and engagement information associated with multiple previously posted posts are used to generate an estimated success level of the post. Depending on the estimated success level for the post, a post property may be modified and the post with the modified property replaces the post on the platform.