| CPC G06F 18/285 (2023.01) [G06F 18/2113 (2023.01); G06F 18/2178 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)] | 20 Claims |
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1. A computer-implemented method comprising:
obtaining media content item analytics data, wherein the media content item analytics data include presentations of media content items to users and lengths of time the users engaged with the media content items that were presented;
iteratively repeating until an evaluation criterion is satisfied:
(i) generating a surrogate machine learning model of the media content item analytics data, wherein the surrogate machine learning model describes a distribution of the lengths of time,
(ii) based on samples from the distribution of the lengths of time, determining scores for a plurality of candidate machine learning models, wherein the plurality of candidate machine learning models each provide presentations of further media content items, and wherein the scores reflect how well the plurality of candidate machine learning models predict further lengths of time of engagement with the media content items that were presented,
(iii) based on the scores, selecting a highest-scoring machine learning model from the plurality of candidate machine learning models,
(iv) updating the media content item analytics data with additional media content item analytics data from online use of the highest scoring machine learning model;
in response to the evaluation criterion being satisfied, deploying a current highest-scoring machine learning model for online use; and
using the current highest-scoring machine learning model to select particular media content items for online streaming to a particular user.
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