| CPC H04N 21/44204 (2013.01) [H04N 21/44213 (2013.01); H04N 21/4532 (2013.01)] | 18 Claims |
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1. A computer implemented method of predicting a probability of viewership by a device, the method comprising:
receiving, over an interval, information associated with the viewership by the device;
merging the received information with source of content displayed on the device and information associated with one or more partnering devices to track content viewership and generate merged information;
aggregating the merged information at an increment to generate aggregated data;
providing the aggregated data as training data to a first model by selecting features of the aggregated data as the training data to train the first model, wherein the first model is trained to perform probability prediction of content viewership on the device;
generating one or more metrics associated with first model output;
comparing the one or more metrics against a baseline model generated from the aggregated data to track performance of the first model output;
providing the selected features as input training data to train a second model as a network viewership model, wherein the second model is trained to perform probability prediction of network viewership on the device; and
performing evaluation of the second model for feature correlations and generating a second model score based on the evaluation of the second model,
wherein the probability prediction of the network viewership by the second model is performed only after the device is predicted to be viewed under the content viewership prediction as performed by the first model,
wherein the one or more partnering devices are identified from a device list that is updated on a periodic basis, wherein the device list comprises a panel of devices that provide accurate content information, and the one or more partnering devices are categorized by make and model, and
wherein the first model performs the probability prediction of the content viewership on the device.
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