Method and system for quantifying user interactions with web advertisements
First Claim
Patent Images
1. A method comprising:
- determining, by one or more processors, one or more first features of a web document;
determining, by the one or more processors, relevance scores for individual ones of a set of two or more pre-existing expert statistical models based at least in part on comparison between the one or more first features of the web document and one or more second features of the set of two or more pre-existing expert statistical models, wherein at least one of the individual ones of the set of two or more pre-existing expert statistical models comprises a statistical model to assess at least the one or more first features of the web document;
selecting, by the one or more processors, one or more expert statistical models, from the set of two or more pre-existing expert statistical models based, at least in part, on the relevance scores;
determining, by the one or more processors, weightings for the one or more selected expert statistical models based, at least in part, on the relevance scores for the one or more expert statistical models;
assessing, by the one or more processors, the at least the one or more first features of the web document based, at least in part, on the one or more selected expert statistical models; and
estimating, by the one or more processors, a click-through-rate probability for a web advertisement to be placed on the web document based on the weightings for the one or more selected expert statistical models.
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Abstract
Methods and systems are provided that may be used to determine a probability of whether a visitor to a web document is likely to click on a web advertisement. An exemplary method may include detecting one or more features in a web document. One or more expert statistical models to which the web document belongs may be determined and associated weightings may be determined based, at least in part, on the one or more features detected. A click-through-rate probability for a web advertisement to be placed on the web document may be estimated based on the one or more expert statistical models.
26 Citations
18 Claims
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1. A method comprising:
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determining, by one or more processors, one or more first features of a web document; determining, by the one or more processors, relevance scores for individual ones of a set of two or more pre-existing expert statistical models based at least in part on comparison between the one or more first features of the web document and one or more second features of the set of two or more pre-existing expert statistical models, wherein at least one of the individual ones of the set of two or more pre-existing expert statistical models comprises a statistical model to assess at least the one or more first features of the web document; selecting, by the one or more processors, one or more expert statistical models, from the set of two or more pre-existing expert statistical models based, at least in part, on the relevance scores; determining, by the one or more processors, weightings for the one or more selected expert statistical models based, at least in part, on the relevance scores for the one or more expert statistical models; assessing, by the one or more processors, the at least the one or more first features of the web document based, at least in part, on the one or more selected expert statistical models; and estimating, by the one or more processors, a click-through-rate probability for a web advertisement to be placed on the web document based on the weightings for the one or more selected expert statistical models. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. An article comprising:
a non-transitory storage medium comprising machine-readable instructions stored thereon which are executable by one or more processors to; detect one or more first features of a web document; determine relevance scores for individual ones of a set of two or more pre-existing expert statistical models based at least in part on comparison between the one or more first features of the web document and one or more second features of the set of two or more pre-existing expert statistical models, wherein at least one of the individual ones of the set of two or more pre-existing expert statistical models comprises a statistical model to assess at least the one or more first features of the web document; select one or more expert statistical models, from the set of two or more pre-existing expert statistical models based, at least in part, on the relevance scores; determine weightings for one or more selected expert statistical models based, at least in part, on the one or more relevance scores for the expert statistical models; assess the at least the one or more first features of the web document based, at least in part, on the one or more selected expert statistical models; and estimate, based on the weightings for the one or more selected expert statistical models, a click-through-rate probability for a web advertisement to be placed on the web document. - View Dependent Claims (9, 10, 11, 12)
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13. A system comprising:
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a computing platform comprising one or more processors to; detect one or more first features of a web document; determine relevance scores for individual ones of a set of two or more pre-existing expert statistical models based at least in part on comparison between the one or more first features of the web document and one or more second features of the set of two or more pre-existing expert statistical models, wherein at least one of the individual ones of the set of two or more pre-existing expert statistical models comprises a statistical model to assess at least the one or more first features of the web document; select one or more expert statistical models, from the set of two or more pre-existing expert statistical models based, at least in part, on the relevance scores; determine weightings for one or more selected expert statistical models based, at least in part, on the one or more relevance scores for the expert statistical models; assess the at least the one or more first features of the web document based, at least in part, on the one or more selected expert statistical models; and estimate, based on the weightings for the one or more selected expert statistical models, a click-through-rate probability for a web advertisement to be placed on the web document. - View Dependent Claims (14, 15, 16, 17, 18)
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Specification