US 2020/0401949 A1Published application

Optimizing machine learned models based on dwell time of networked-transmitted content items

AssigneeMicrosoft Technology Licensing, LLC

InventorsSiddharth Dangi, Manas Somaiya, Ying Xuan, Bonnie Barrilleaux

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Abstract

Techniques for optimizing machine-learned models based on dwell time of network-transmitted content items are provided. In one technique, impression data and selection data are used train a selection prediction model. For each impression, a dwell time associated with that impression is determined and compared to a skip time. If the dwell time is less than the skip time, then a first training label that indicates that the impression is skipped is associated with the impression. If the dwell time is greater than the skip time, then a second training label that indicates that the impression is not skipped is associated with the impression. These training labels are used to train a skip prediction model. The selection prediction model and the skip prediction model are used in a content item selection event to generate a score for each candidate content item. The scores are used to select a content item.

Claims20

Dependent claims

A method comprising:

storing impression data that indicates a plurality of impressions of a plurality of content items;
storing selection data that indicates a plurality of selections of a subset of the plurality of content items;
using one or more first machine learning techniques to train, based on the impression data and the selection data, a selection prediction model;
for each impression of the plurality of impressions:
determining a dwell time associated with said each impression;
storing, in association with said each impression, a first label that indicates that a content item associated with said each impression is skipped if the dwell time is less than a skip time;
storing, in association with said each impression, a second label that indicates that the content item associated with said each impression is not skipped if the dwell time is greater than the skip time;
using one or more second machine learning techniques to train, based on the first and second labels associated with the plurality of impressions, a skip prediction model;
in response to receiving a content request:
identifying a plurality of candidate content items and an entity that initiated the content request;
for each candidate content item in the plurality of candidate content items:
determining a first prediction based on the selection prediction model and the entity;
determining a second prediction based on the skip prediction model and the entity;
generating a score based on the first prediction and the second prediction;
selecting a particular content item from among the plurality of candidate content items based on the score generated for each candidate content item in the plurality of candidate content items;
causing the particular content item to be transmitted over a computer network in response to the content request;
wherein the method is performed by one or more computing devices.

14 more elements

A method comprising:

storing impression data that indicates a plurality of impressions of a plurality of content items;
storing selection data that indicates a plurality of selections of a subset of the plurality of content items;
generating a plurality of training instances based on the plurality of selections and the plurality of impressions;
for each selection in the plurality of selections:
determining a dwell time associated with said each selection;
storing, in association with a training instance, of the plurality of training instances, corresponding to said each selection, a first label that indicates that a content item, associated with said each selection, that was selected by an entity was not selected if the dwell time is less than a bounce time;
storing, in association with the training instance corresponding to said each selection, a second label that indicates that the content item associated with said each impression was selected if the dwell time is greater than the bounce time;
using one or more first machine learning techniques to train, based on the plurality of training instances and the first and second labels, a selection prediction model;
wherein the method is performed by one or more computing devices.

6 more elements

A method comprising:

storing impression data that indicates a plurality of impressions of a plurality of content items, wherein each impression of the plurality of impressions indicates an impression of a content item of the plurality of content items;
generating a plurality of training instances based on the plurality of impressions, wherein each training instance of the plurality of training instances corresponds to a different impression of the plurality of impressions;
for each training instance of the plurality of training instances:
determining a dwell time that indicates an amount of time that a first content item associated with said each impression was presented to a viewer;
determining, based on the dwell time, a weight associated with the training instance;
using one or more first machine learning techniques to train, based on the plurality of training instances and the weight determined for each training instance, a prediction model;
wherein the method is performed by one or more computing devices.

4 more elements

Dependencies are inferred from the claim text.