Using Linear and Log-Linear Model Combinations for Estimating Probabilities of Events
First Claim
1. A computer-implemented method for combining probability of click models in an online advertising system, said method comprising:
- receiving, at a computer, at least one feature set slice;
training, in a computer, a plurality of slice predictive models, the slice predictive models corresponding to at least a portion of the features in the at least one feature set slice;
weighting, in a computer, at least two of the plurality of slice predictive models by overlaying a weighted distribution model over the plurality of slice predictive models; and
calculating, in a computer, a combined predictive model based on the weighted distribution model and the at least two of the plurality of slice predictive models.
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Abstract
A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination. Using an appropriate weighted distribution model, the combined predictive model reliably yields predictive estimates of occurrence of click events that are at least as good as the best predictive model in the slice-wise predictive model set.
45 Citations
20 Claims
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1. A computer-implemented method for combining probability of click models in an online advertising system, said method comprising:
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receiving, at a computer, at least one feature set slice; training, in a computer, a plurality of slice predictive models, the slice predictive models corresponding to at least a portion of the features in the at least one feature set slice; weighting, in a computer, at least two of the plurality of slice predictive models by overlaying a weighted distribution model over the plurality of slice predictive models; and calculating, in a computer, a combined predictive model based on the weighted distribution model and the at least two of the plurality of slice predictive models. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer readable medium comprising a set of instructions which, when executed by a computer, cause the computer to combine probability of click models in an online advertising system, said instructions for:
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receiving, at a computer, at least one feature set slice; training, in a computer, a plurality of slice predictive models, the slice predictive models corresponding to at least a portion of the features in the at least one feature set slice; weighting, in a computer, at least two of the plurality of slice predictive models by overlaying a weighted distribution model over the plurality of slice predictive models; and calculating a combined predictive model based on the weighted distribution model and the at least two of the plurality of slice predictive models. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. An advertising network for combining probability of click models in an online advertising system, comprising:
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a module for receiving at least one feature set slice; a module for training a plurality of slice predictive models, the slice predictive models corresponding to at least a portion of the features in the at least one feature set slice; a module for weighting at least two of the plurality of slice predictive models by overlaying a weighted distribution model over the plurality of slice predictive models; and a module for calculating a combined predictive model based on the weighted distribution model and the at least two of the plurality of slice predictive models. - View Dependent Claims (20)
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Specification