A system and methods for evaluating whether a public review matches another review is disclosed. The method includes obtaining a private review and a public review of a business. A likelihood the public review matches the private review is evaluated. The evaluation includes evaluating a match distance between text bodies, reviewer names, review dates and ratings in the public review and the private review. Evaluation rules are applied to determine the likelihood of a match.

Data transformation caching in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to a dataset; generating, in dependence upon the one or more transformations, a transformed dataset; storing, within one or more of the storage systems, the transformed dataset; receiving a plurality of requests to transmit the transformed dataset to one or more of the GPU servers; and responsive to each request, transmitting, from the one or more storage systems to the one or more GPU servers without re-performing the one or more transformations on the dataset, the transformed dataset.

A method includes receiving a voice request to generate synthesized voiceover speech for a target advertisement having one or more advertising campaign attributes. The method also includes generating, based on the one or more advertising campaign attributes, a voiceover script that includes a sequence of text for the synthesized voiceover speech. The method also includes generating, using a text-to-speech (TTS) system, the synthesized voiceover speech. The TTS system is configured to receive, as input, the sequence of text for the voiceover script and generate, as output, the synthesized voiceover speech. Here, the synthesized voiceover speech has speech characteristics specified by a target TTS vertical. The method also includes overlaying the synthesized voiceover speech on the target advertisement.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated management of campaigns using scripted rules.

Systems and methods for conducting a test on a third-party testing platform are provided. A networked system causes presentation of a setup user interface to a third-party user, whereby the setup user interface includes a field for indicating an attribute of a publication to be tested. The networked system receives, via the setup user interface, an indication of the attribute, a subject to be tested, and one or more test parameters. The networked system applies the attribute change to a first version of the publication to generate a second version of the publication. The first version is presented to a first subset of potential users and the second version is presented to a second subset of potential users. Interactions with both the first version and the second version are monitored and analyzed to determine results of the test. The results are then presented to the third-party user.

A system and method for delivering promotional rewards to a customer mobile device comprising a promotional reward wheel, a media server, and messaging application operating on the customer's mobile device. A customer interacts with the promotional reward wheel and the system captures that interaction and responsive to the promotional reward that was won by the interaction, generates a message on the customer's mobile device with a means to claim the reward by sending the generated message to the appropriate message endpoint.

In certain embodiments, the present disclosure relates to generating a campaign for a promoted electronic game. According to one embodiment, a method for generating a campaign for a promoted electronic game can comprise obtaining content data for each player of a plurality of players of an electronic game other than the promoted electronic game and campaign data for each campaign of a plurality of campaigns other than the campaign for the promoted game. Recommendation data can be generated for the campaign for the promoted electronic game. The recommendation data can indicate a plurality of possible campaigns, each campaign of the plurality of possible campaigns comprising a plurality of actions related to the promoted electronic game. Generating the recommendation data can comprise applying a recommendation model to the content data and the campaign data. A user interface comprising details of the generated recommendation data can then be presented

In an aspect, a request to display information on a client device of a user can be received. A plurality of features associated with the request can be extracted. The plurality of features can include at least one feature characterizing the user and at least an additional feature characterizing the client device. A feature vector based on the features associated with the request can be generated. A predicted response value of the user to a content presentation can be generated using a predictive model and the feature vector. The predicted response value can characterize a likelihood of the user interacting with the content presentation. A request response value can be determined based on a content presentation impression value and the feature vector. The request response value and the content presentation can be transmitted for display on the client device of the user.

A method, system and/or computer usable program product for automatically managing the conveying of messages among multiple communication channels including (i) receiving, from a first computing system, an on-line message addressed to a user, (ii) automatically categorizing the message among a predetermined set of message categories stored in memory, (iii) identifying a set of on-line message channels preselected by the addressee user for receiving messages for each of the predetermined set of message categories, (iv) identifying a set of performance metrics stored in memory for optimizing message channel selection, (v) utilizing the performance metrics to automatically select an optimum message channel from the preselected message channels for sending the categorized message to a second computing system of the addressee user, (vi) automatically formatting the categorized message for the optimum message channel, and (vii) sending the formatted message on-line to the second computing system of the addressee user across the optimum message channel.

The present teaching relates to generating combination distributions for ads. A prediction model is obtained via machine learning with respect to a criterion. Training data are associated with multiple ads each having multiple attributes, and include combinations with recorded performance for each ad. Each combination has multiple assets representing respective attributes of an ad. Using the prediction model, performance of each combination of each ad can be predicted and used for generating combination distributions for the ads. Such generated combination distributions are then sent to an explore/exploit layer (EEL) at a frontend ad serving engine so that it can draw a combination associated with an auction winning ad for rendering on a webpage viewed by a user on a user device.