SIMILARITY FUNCTION IN ONLINE ADVERTISING BID OPTIMIZATION
First Claim
1. A method for use in association with an auction-based online advertising exchange, the method comprising:
- using one or more computers, obtaining a set of information comprising;
historical advertisement impression information associated with a set of previously served advertisement impressions, comprising profile information and revenue-related performance information;
forecasted revenue-related performance information relating to each of a set of possible advertisement impressions over a period of time; and
information elating to a first advertisement impression serving opportunity to be served during the period of time;
using one or more computers and using at least a portion of the set of information, using a machine learning-based technique, comprising using a similarity function, in determining at least one advertisement impression of the set of possible advertisement impressions that is most similar to the first advertisement impression serving opportunity for a purpose of determining an optimized bid relating to the first advertisement impression serving opportunity, wherein weighting relating to advertisement features is determined in a nonlinear fashion relative to individual features;
using one or more computers, determining an optimized bid relating to the first advertisement impression serving opportunity, based at least in part on forecasted revenue-related performance information relating to the at least one advertisement impression; and
using one or more computers, storing optimized bid information relating to the optimized bid.
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Abstract
The present invention provides methods and systems for use in bid optimization in connection with advertisement serving impression opportunities available in an auction-based online advertising exchange. Methods are presented in which, based in part on historical advertisement performance information, a Kalman filter-based model is used in forecasting performance of a set of possible advertisement impressions served over a future period of time. Forecasted performance information is used in determining an optimized bid in connection with an available opportunity. A similarity function, including non-linearly determined feature weighting, can be used in determining most similar forecasted impressions to the available opportunity.
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Citations
20 Claims
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1. A method for use in association with an auction-based online advertising exchange, the method comprising:
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using one or more computers, obtaining a set of information comprising; historical advertisement impression information associated with a set of previously served advertisement impressions, comprising profile information and revenue-related performance information; forecasted revenue-related performance information relating to each of a set of possible advertisement impressions over a period of time; and information elating to a first advertisement impression serving opportunity to be served during the period of time; using one or more computers and using at least a portion of the set of information, using a machine learning-based technique, comprising using a similarity function, in determining at least one advertisement impression of the set of possible advertisement impressions that is most similar to the first advertisement impression serving opportunity for a purpose of determining an optimized bid relating to the first advertisement impression serving opportunity, wherein weighting relating to advertisement features is determined in a nonlinear fashion relative to individual features; using one or more computers, determining an optimized bid relating to the first advertisement impression serving opportunity, based at least in part on forecasted revenue-related performance information relating to the at least one advertisement impression; and using one or more computers, storing optimized bid information relating to the optimized bid. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A system for use in an online advertising exchange, comprising
one or more server computers coupled to a network; - and
one or more databases coupled to the one or more server computers; wherein the one or more server computers are for; obtaining a set of information comprising; historical advertisement impression information associated with a set of previously served advertisement impressions, comprising profile information and revenue-related performance information; forecasted revenue-related performance information relating to each of a set of possible advertisement impressions over a period of time; and information relating to a first advertisement impression serving opportunity to be served during the period of time; using at least a portion of the set of information, using a machine learning-based technique, comprising using a similarity function, in determining at least one advertisement impression of the set of possible advertisement impressions that is most similar to the first advertisement impression serving opportunity for a purpose of determining an optimized bid relating to the first advertisement impression serving opportunity, wherein weighting relating to advertisement features is determined in a nonlinear fashion relative to individual features; determining an optimized bid relating to the first advertisement impression serving opportunity, based at least in part on forecasted revenue-related performance information relating to the at least one advertisement impression; and storing optimized bid information relating to the optimized bid in at least one of the one or more databases. - View Dependent Claims (17, 18)
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19. A computer readable medium or media containing instructions for executing a method comprising:
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using one or more computers, obtaining a set of information comprising; historical advertisement impression information associated with a set of previously served advertisement impressions, comprising profile information and revenue-related performance information; forecasted revenue-related performance information relating to each of a set of possible advertisement impressions over a period of time; and information relating to a first advertisement impression serving opportunity to be served during the period of time; using one or more computers and using at least a portion of the set of information, using a machine learning-based technique, comprising using a similarity function, in determining at least one advertisement impression of the set of possible advertisement impressions that is most similar to the first advertisement impression serving opportunity for a purpose of determining an optimized bid relating to the first advertisement impression serving opportunity, wherein weighting relating to advertisement features is determined in a nonlinear fashion relative to individual features and relates to importance in similarity analysis using the similarity function; using one or more computers, determining an optimized bid relating to the first advertisement impression serving opportunity, based at least in part on forecasted revenue-related performance information relating to the at least one advertisement impression; using one or more computers, storing optimized bid information relating to the optimized bid; and using one or more computers, implementing bidding in accordance with the optimized bid. - View Dependent Claims (20)
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Specification