Abstract: A method includes providing a Fibonacci confidence interval level on user program viewership data to distinguish user interest level in different linear entertainment programs. The method also includes creating a behavior shift feature space for acquiring information about user behavior over time based on a behavior sequence, a first derivative on the behavior sequence, and a second derivative on the behavior sequence. The method further includes utilizing, based on a trained machine learning model, a transformer structure and attention to map user data from the behavior shift feature space into a prediction of the Fibonacci confidence interval level.

Abstract: A method includes providing a Fibonacci confidence interval level on user program viewership data to distinguish user interest level in different linear entertainment programs. The method also includes creating a behavior shift feature space for acquiring information about user behavior over time based on a behavior sequence, a first derivative on the behavior sequence, and a second derivative on the behavior sequence. The method further includes utilizing, based on a trained machine learning model, a transformer structure and attention to map user data from the behavior shift feature space into a prediction of the Fibonacci confidence interval level.

Abstract: A method includes obtaining, based on a sequential graph-based model, gaming exposure information over time, where the gaming exposure information includes device-level preferences and household-level preferences. The method also includes combining one or more raw user behavior sessions into a gameplay session based on the obtained gaming exposure information. The method further includes providing a scoring metric to (i) check an extent of multi-matching in the obtained gaming exposure information and (ii) remove untrustworthy gaming exposures from the obtained gaming exposure information. In addition, the method includes generating, based on a feature engineering pipeline, one or more game segments running in a production environment, where the one or more game segments are identified for ancillary content based on inferences by a machine learning model trained using the gaming exposure information.

Abstract: A method implemented by one or more computing systems includes accessing content viewing data associated with a first user account, wherein the first user account is associated with one or more client devices. The content viewing data includes temporal-based content viewing data. The method further includes determining, using one or more sequence models, a set of content viewing features based on the temporal-based content viewing data, and concatenating the content viewing features into a single computational array. The method further includes providing, through one or more dense layers of a deep-learning model, the single computational array to an output layer of the deep-learning model, and calculating, based on the output layer, one or more probabilities for one or more labels for the first user account. Each label includes a predicted attribute for the first user account.

Abstract: A method includes obtaining (i) device data associated with multiple electronic devices and (ii) advertisement data associated with one or more advertisement campaigns. The method also includes identifying first features and second features, where the first features correspond to the device data and the second features correspond to the advertisement data. The method further includes generating a graph relating usage history of the multiple electronic devices and one or more advertisement genres. The method also includes identifying a specified electronic device from among the multiple electronic devices and an advertisement segment from among the one or more advertisement campaigns using the first features, the second features, and the graph. In addition, the method includes providing the advertisement segment to the specified electronic device.

Abstract: An electronic device includes at least one processor configured to obtain user data associated with a plurality of devices from multiple data sources. The at least one processor is also configured to determine a static weight for each of the plurality of devices based on at least one source of the multiple data sources. The at least one processor is further configured to identify a portion of the plurality of devices that represents the plurality of devices based on the static weight and a dynamic weight. In addition, the at least one processor is configured to determine the dynamic weight for each of the portion of the plurality of devices while the portion of the plurality of devices is identified, where the dynamic weight is based on one or more sources of the multiple data sources.

Abstract: A method includes obtaining, using at least one processor of an electronic device, one or more instance level supervised artificial intelligence (AI) models. The method also includes obtaining, using the at least one processor, aggregated level label information related to the one or more instance level supervised AI models. The method further includes obtaining, using the at least one processor, instance level feature information related to the one or more instance level supervised AI models. In addition, the method includes training, using the at least one processor, the one or more instance level supervised AI models using the instance level feature information and the aggregated level label information to obtain one or more trained instance level supervised AI models.

Abstract: A method implemented by one or more computing systems includes accessing content viewing data associated with a first user account, wherein the first user account is associated with one or more client devices. The content viewing data includes temporal-based content viewing data. The method further includes determining, using one or more sequence models, a set of content viewing features based on the temporal-based content viewing data, and concatenating the content viewing features into a single computational array. The method further includes providing, through one or more dense layers of a deep-learning model, the single computational array to an output layer of the deep-learning model, and calculating, based on the output layer, one or more probabilities for one or more labels for the first user account. Each label includes a predicted attribute for the first user account.