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Expansion of targeting criteria based on advertisement performance

  • US 10,318,983 B2
  • Filed: 07/18/2014
  • Issued: 06/11/2019
  • Est. Priority Date: 07/18/2014
  • Status: Active Grant
First Claim
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1. A computer-implemented method comprising:

  • receiving an advertisement request including targeting criteria specifying a target audience of users of an online system eligible to be presented with the advertisement request, the advertisement request specifying a goal and a time interval for achieving the goal;

    determining an actual performance of the advertisement request during a time period within the time interval based on presentation of advertisement content included in the advertisement request to users of an online system;

    comparing the actual performance of the advertisement request to a portion of the goal associated with the time period; and

    responsive to the comparison determining that the actual performance of the advertisement request does not satisfy the portion of the goal associated with the time period;

    identifying a cluster group that is associated with the targeting criteria included in the advertisement request;

    selecting a sample set of users for determining a cluster cutoff score corresponding to a target ranking threshold based on the goal;

    determining a set of sampling scores for the sample set of users;

    determining a ranking of the sample set of users based on the respective sampling scores;

    selecting a subset of the sample scores above a target ranking threshold and within a threshold amount of the target ranking threshold;

    performing a data fit between the subset of the sample scores and the ranking to estimate a cluster cutoff score corresponding to the target ranking threshold, wherein performing the data fit to estimate the cluster cutoff score comprises;

    performing the data fit on a plurality of computational nodes to generate a plurality of results;

    sharing the results between neighboring computational nodes; and

    selecting the cluster cutoff score in response to a consensus number of computational nodes agreeing on the cluster cutoff score;

    identifying a target user outside of the target audience that has user characteristics failing to satisfy the targeting criteria;

    determining, based on a cluster model, a cluster score for the user representing an affinity measure between characteristics and the targeting criteria for the cluster group;

    comparing the cluster score to the cluster cutoff score to determine if the target user is a member of the cluster group; and

    responsive to determining that the target user is a member of the cluster group, adding the target user to the target audience eligible to be presented with the advertisement request.

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