Techniques are described for machine learning-based generation of target segments is leveraged in a digital medium environment. A segment targeting system generates training data to train a machine learning model to predict strength of correlation between a set of users and a defined demographic. Further, a machine learning model is trained with visit statistics for the users to predict the likelihood that the users will visit a particular digital content platform. Those users with the highest predicted correlation with the defined demographic and the highest likelihood to visit the digital content platform can be selected and placed within a target segment, and digital content targeted to the defined demographic can be delivered to users in the target segment.

An example apparatus includes a first query processor to obtain a first hash key, first qualifier data and first value data associated with the first hash key, the first value data including a second hash key and a third hash key; a candidate qualifier to use the first value data to prequalify the first hash key as a candidate for subsequent signature processing associated with a first site signature, the candidate qualifier to: determine prequalification of the first hash key is successful in response to a determination that the second hash key matches a second site signature preceding the first site signature in time and that the third hash key matches a third site signature following the first site signature in time; and determine the prequalification of the first hash key is unsuccessful in response to a determination that at least one of the second hash key fails to match the second site signature or the third hash key fails to match the third site signature.

An electronic device including a display device configured to render first content. The electronic device is communicably coupled to the display device and controls one or more imaging devices to receive one or more images from the one or more imaging devices. The electronic device further determines a first position of one or more living objects within a pre-defined region from the display device, based on the received one or more images and the rendered first content. The electronic device further controls an orientation of the display device towards the determined first position of the one or more living objects.

Methods, apparatus, and systems to collect audience measurement data are disclosed. Disclosed example systems include first means for processing to collect (i) first media identification data corresponding to a first time period and (ii) second media identification data corresponding to a second time period after the first time period, the first media identification data and the second media identification data associated with a monitored location and second means for processing to develop behavior data based on the first media identification data and people meter data collected during the first time period with a people meter associated with the monitored location, the people meter to be deactivated during the second time period and identify an audience member associated with the second media identification data based on the behavior data and without access to any people meter data corresponding to the second time period.

Example methods and apparatus to replicate panelists using a local minimum solution of an integer least squares problem are disclosed. An example apparatus includes memory; and processor circuitry to execute computer readable instructions to: determine weight adjustments based on a ratio of (A) a first dot product of (i) attribute data and (ii) a difference between first aggregate panelist data and second seed panelist data, and (B) a second dot product of the attribute data and the attribute data, the first aggregate panelist data based on the attribute data, the attribute data corresponding to a seed panel; determine weight estimates based on the weight adjustments, the weight estimates to replicate respective seed panelists in the seed panel; and replicate respective ones of the seed panelists based on the weight estimates to generate a synthetic audience representative of return path data reported by a plurality of media devices.

Systems and methods for determining, based on recommendations provided by users that have consumed a media asset, which consumption options may be configured on a media device such that when configured enhance the user viewing experience for a specific media asset. The method includes accessing comments posted by other users that have consumed the media asset. The comments are analyzed to determine a consumption option recommendation. If the number of comments meet a threshold value, then the system either automatically configures the media device or configures the media device upon user approval with the recommended consumption option. The recommendation to configure a consumption option on the media device is made only if the recommendation is supported by the media device. The system also detects through audio and image analysis which users are consuming the media asset and accordingly configures the consumption options to their preferences.

Techniques for regional viewership predictions of broadcast events such as live broadcast professional sporting events. The techniques can make the predictions without a direct response variable such as regional viewership data for training a prediction model. Instead, in one technique, demand information for a good or service is used. From the demand information, a derivative demand for the good or service relative to a normal demand is determined. A regression framework is used to learn relationships between the derivative demand for the good or service and features of past live broadcast sporting events. This results in a matrix of feature weights. A non-parametric mixture framework is then used to find a set of feature weights that can be applied to features of future broadcast events to generate regional viewership predictions for the events.

According to examples, a system for providing dynamic digital content may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to receive a plurality of data feeds. The processor may further analyze the data feeds to identify values for parameterized variables. A plurality of deep learning (DL) models can be trained to obtain product attribute data from the data feeds. The processor may then identify rules or triggers based on the values of the parameterized variables. The rules and/or triggers cause the processor to dynamically generate or select digital content and transmit the digital content to user communication devices of selected audience.

A system is provided for dynamic scheduling and channel creation based on external data. Audience-based parameters comprising demographics data, targeted audience data, device type data, and trending information that includes media items based on current trend in social network platform are received from external data source. A media item to be inserted in first media feed of first channel is determined based on a plurality of pre-encoded media content, metadata, audience-based parameters and defined parameters. The plurality of pre-encoded media content is segmented into a plurality of media segments, each corresponding to different quality level and content encryption mode. A second channel is generated from first channel based on audience-based parameters, insertion of media item, and second programming schedule. The second programming schedule is generated from modification of first programming schedule based on audience-based parameters. The media item is delivered, in viewable format, in second media feed to consumer device.

Aspects of the subject disclosure may include, for example, a device, that has a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, where the operations include detecting each individual of an audience viewing media content on user equipment; retrieving a user profile for each individual of the audience resulting in user profiles; creating a group profile from the user profiles; determining, based on the group profile, a recommendation for viewing a candidate media content; and providing the recommendation to the user equipment for the audience. Other embodiments are disclosed.