A system of assessing deployments in a network-based media system is provided herein. The system include a data storage system storing observation vectors, each observation vector being associated with an outcome indicator, and a processing device in communication with the data storage system to receive and store observation vectors and associated outcome indicators. The processing device performs operations including communicating with an endpoint device of a user to obtain information associated with the endpoint device; and transmitting an instance of a variable user interface to the endpoint device for presentation to the user via the endpoint device based on the stored observation vectors, the stored associated outcome indicators, and the obtained information associated with the endpoint device. Related systems and methods are also disclosed.

The disclosed embodiments provide a system that facilitates selecting a message to be presented to users based on a statistically valid hypothesis test. During operation, the system runs a hypothesis test by presenting alternate versions of a message to a test set of users and receives user-feedback data. Next, the system obtains a significance level for the test and determines a number of independent data subsets associated with data from the test. The system subsequently uses the significance level and the number of independent data subsets to calculate an individual significance level for each independent data subset. The system then uses the individual significance levels to calculate an amount of user-feedback data required to achieve the significance level during the test, and selects one of the alternate versions of the message by analyzing the calculated amount of user-feedback data during the test. Finally, the system presents the selected version.

The embodiments set forth techniques for managing advertisement attributions. A first technique can be implemented by an app store application, and include the steps of (1) receiving, from a first user application, a request to view a second user application, where the request includes a set of digitally-signed parameters that are specific to an advertising campaign (provided by an advertisement network) for the second user application that is presented by the first user application. In turn, and in response to identifying that the second user application satisfies at least one criterion, the app store application provides the set of digitally-signed parameters to an advertisement metrics manager that: (i) verifies the set of digitally-signed parameters, and (ii) indicates, to the advertisement network, that business logic should be carried out in association with the first user application and the second user application. A second technique for managing advertisement attributions is also disclosed.

A system and method for normalizing campaign data gathered from a plurality of advertising platforms. The method comprises receiving campaign data related to at least one campaign gathered from a plurality of advertising platforms; mapping data dimensions in the received campaign data to a marketing data model to produce a dataset that is organized and functions as the marketing data model; normalizing data values in the dataset according to a unified notation defined for each of the data dimensions in the marketing data model; and optimizing the normalized dataset to allow faster manipulation of data.

A system and method for the federation of content items of a social network based on personalized relevance includes obtaining content items from first and second content item sources. Profile data for a member of the social network is obtained from the electronic data storage. A relevance score of the content item to the profile data of the member is determined for each of the content items. A utility value is determined based on the selection value, the value metric for content items from the first content item source, and the relevance score. A user device associated with the member displays the content items based on their respective utility values.

An information processing apparatus includes an acquisition unit acquires a plurality of pieces of content information from a plurality of applications capable of providing content, an output control unit reproduces and outputs at least part of the plurality of pieces of content information acquired by the acquisition unit to a mobile object in accordance with a priority determined based on a predetermined condition, and a generation unit that generates, as output target information to be output to the application that provided the content information, an information indicating whether or not the content information has been reproduced and output to the mobile object.

Methods, systems, and computer programs are presented for eliminating bias while training an ML model using training data that includes past experimental data. One method includes accessing experiment results, for A/B testing of a first model, that comprise information regarding engagement with a first set of items presented to users, each item being presented within an ordered list of results. A position bias is calculated for positions within the ordered list of results where the items were presented. A machine-learning program is trained to obtain a second model using a training set comprising values for features that include the calculated position bias. The method includes detecting a second set of items to be ranked for presentation to a first user, and calculates, using the second model, a relevance score for the second set of items, which are ranked based on the respective relevance score and presented on a display.

An online system generates a set of genomic representations, each including multiple genes, in which each gene represents users assigned to a control or test group for performing a test. A metric is identified based on a treatment associated with the test group and a score for each representation is computed based on a difference between two values, in which each value is based on the metric associated with users assigned to the test or control group. A propagation process is executed by identifying representations having at least a threshold score, propagating genes included in the representations to an additional set of representations through recombination and/or mutation, and computing the score for each additional representation. The propagation process is repeated for each additional set of representations until stopping criteria are met and a representation is selected based on scores associated with one or more representations.

Disclosed are methods and systems for generating targeted advertising campaigns for a business-to-business (B2B) company. The method comprises retrieving historical data on one or more historical experiments; determining, using a prediction engine, a prediction of a goal metric by finding a pattern in a historical metric that influences the goal metric, where the historical metric is included in the historical data; determining, using the prediction engine, correlations between one or more experimental parameters and the goal metric, based on the prediction of the goal metric; training, using the prediction engine, two or more experimental parameter models for the goal metric, based on the correlations between the one or more experimental parameters and the goal metric; generating, using a campaigns engine, one or more new experiments, each associated with the goal metric, and based on the two or more experimental parameter models; and generating, using the campaigns engine, a targeted advising campaign comprising a selected number of the new experiments.

A method includes determining a plurality of harvest content items. The harvest content items are ranked based on a performance metric. Matching criterion aspects of the harvest content items are determined. Aspects of a candidate content item are compared with the plurality of harvest content items according to the matching criterion aspects. A subset of the harvest content items that are similar to the candidate content item is determined. A transformation for the candidate content item is selected and applied to the candidate content item to generate a transformed content item.