Using estimated ad qualities for ad filtering, ranking and promotion
First Claim
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1. A method implemented by one or more processors of a computer system, comprising:
- obtaining, using one or more processors of the computer system, ratings associated with a first group of advertisements, where the ratings, which include a quality of the first group of advertisements, are received from human raters;
observing, using one or more processors of the computer system, multiple different user actions associated with user selection of advertisements of the first group of advertisements;
deriving, using one or more processors of the computer system, a probability model using the observed user actions and the obtained ratings, where the probability model includes a probability function that specifies a probability that an advertisement, of a second group of advertisements, is of a certain quality as a function of multiple different types of user actions, where deriving the probability model comprises;
using at least one of logistic regression, regression trees or boosted stumps to generate the probability model;
using, by one or more processors of the computer system, the probability model to estimate quality scores associated with the second group of advertisements;
calculating, using one or more processors of the computer system, a combination of the estimated quality scores and click through rates associated with advertisements in the second group of advertisements;
filtering, using one or more processors of the computer system, the second group of advertisements based on a comparison of the combination of the estimated quality scores and click through rates to a threshold to generate a subset of advertisements from the second group of advertisements; and
providing, to a user, the subset of advertisements from the second group of advertisements.
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Abstract
A system obtains a first parameter (QP1) associated with a quality of an advertisement among multiple advertisements, where the first quality parameter (QP1) does not include a click through rate (CTR). The system functionally combines the first quality parameter (QP1) with at least one other parameter and uses the functional combination to filter, rank or promote the advertisement among the multiple advertisements.
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Citations
13 Claims
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1. A method implemented by one or more processors of a computer system, comprising:
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obtaining, using one or more processors of the computer system, ratings associated with a first group of advertisements, where the ratings, which include a quality of the first group of advertisements, are received from human raters; observing, using one or more processors of the computer system, multiple different user actions associated with user selection of advertisements of the first group of advertisements; deriving, using one or more processors of the computer system, a probability model using the observed user actions and the obtained ratings, where the probability model includes a probability function that specifies a probability that an advertisement, of a second group of advertisements, is of a certain quality as a function of multiple different types of user actions, where deriving the probability model comprises; using at least one of logistic regression, regression trees or boosted stumps to generate the probability model; using, by one or more processors of the computer system, the probability model to estimate quality scores associated with the second group of advertisements; calculating, using one or more processors of the computer system, a combination of the estimated quality scores and click through rates associated with advertisements in the second group of advertisements; filtering, using one or more processors of the computer system, the second group of advertisements based on a comparison of the combination of the estimated quality scores and click through rates to a threshold to generate a subset of advertisements from the second group of advertisements; and providing, to a user, the subset of advertisements from the second group of advertisements. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system, comprising:
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means for obtaining ratings associated with a first group of advertisements, where the ratings, which include a quality of the first group of advertisements, are received from human raters; means for observing multiple different user actions associated with user selection of advertisements of the first group of advertisements; means for deriving a probability model using the observed user actions and the obtained ratings, where the means for deriving a probability model further comprise; means for using at least one of logistic regression, regression trees or boosted stumps to generate the probability model; means for using the probability model to estimate quality scores associated with a second group of advertisements; means for calculating a combination of the estimated quality scores and click through rates associated with advertisements in the second group of advertisements; means for filtering the second group of advertisements based on a comparison of the combination of the estimated quality scores and click through rates to a threshold to generate a subset of advertisements from the second group of advertisements; and means for providing, to a user, the subset of advertisements from the second group of advertisements. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A computer-readable memory device that stores computer-executable instructions, comprising:
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one or more instructions for obtaining ratings associated with a first group of advertisements, where the ratings, which include a quality of the first group of advertisements, are received from human raters; one or more instructions for observing multiple different user actions associated with user selection of advertisements of the first group of advertisements; one or more instructions for deriving a probability model using the observed user actions and the obtained ratings, where the probability model includes a probability function that specifies a probability that an advertisement, of a second group of advertisements, is of a certain quality as a function of multiple different types of user actions, where the instructions for deriving the probability model comprising; one or more instructions for using at least one of logistic regression, regression trees or boosted stumps to generate the probability model; one or more instructions for using the probability model to estimate quality scores associated with the second group of advertisements; one or more instructions for calculating a combination of the estimated quality scores and click through rates associated with advertisements in the second group of advertisements; one or more instructions for filtering the second group of advertisements based on a comparison of the combination of the estimated quality scores and click through rates to a threshold to generate a subset of advertisements from the second group of advertisements; and one or more instructions for providing, to a user, the subset of advertisements from the second group of advertisements.
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Specification