CPC G06F 11/3438 (2013.01) [G06F 3/04842 (2013.01); G06F 16/95 (2019.01); G06N 20/00 (2019.01)] | 20 Claims |
1. A method, comprising:
identifying a first plurality of sets of event information associated with a first plurality of events, wherein the first plurality of sets of event information comprises:
a second plurality of sets of event information associated with a plurality of accidental click events of the first plurality of events; and
a third plurality of sets of event information associated with a plurality of skip events of the first plurality of events;
determining a plurality of accidental click probabilities associated with a second plurality of events comprising the plurality of accidental click events and the plurality of skip events, wherein the determining the plurality of accidental click probabilities comprises:
determining a first accidental click probability, associated with a first accidental click event of the plurality of accidental click events, based upon a first set of event information associated with the first accidental click event; and
determining a second accidental click probability, associated with a first skip event of the plurality of skip events, based upon a second set of event information associated with the first skip event;
performing machine learning model training, using the first plurality of sets of event information associated with the first plurality of events and a first plurality of labels associated with the first plurality of events, to generate a first machine learning model, wherein:
the first plurality of labels comprises a second plurality of labels associated with the second plurality of events; and
labels of the second plurality of labels are based upon the plurality of accidental click probabilities and comprise:
a first label, associated with the first accidental click event, based upon the first accidental click probability; and
a second label, associated with the first skip event, based upon the second accidental click probability;
receiving a request for content associated with a client device;
responsive to receiving the request for content, determining a plurality of click probabilities associated with a plurality of content items using the first machine learning model; and
selecting, based upon the plurality of click probabilities, a first content item of the plurality of content items for presentation via the client device.
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