AD AUCTION OPTIMIZATION

• US 20120005028A1
• Filed: 06/30/2010
• Published: 01/05/2012
• Est. Priority Date: 06/30/2010
• Status: Abandoned Application

• Current Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Original Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Inventors: Stone, Peter, Pardoe, David, Chakraborty, Doran
• Application Number:
  US12/827,906
• Publication Number:
  US 20120005028A1
• Time in Patent Office: Days
• US Class Current: 705/14.71
• CPC Class Codes: G06Q 30/0275 Auctions
• CPC Class Codes: G06Q 30/08 Auctions

Abstract

The present disclosure generally relates to ad auction optimization. In some examples, methods, systems, and computer programs for ad auction optimization using machine learning algorithms to estimate a likelihood that a consumer will purchase an advertised product and balance long term and short term goals to determine modeled data for a keyword in an auction are described.

Claims

1. A system for ad auction optimization comprising:

an estimation and prediction module configured to receive information and use machine logic on the information to estimate a likelihood a consumer will purchase an advertised item, the estimation and prediction module being further configured to output estimation and prediction module information; and

an optimization module configured to receive estimation and prediction module information from the estimation and prediction module and other information and use machine logic on the received information to determine a bid amount for a keyword in an auction, wherein the optimization module includes a single-day optimizer and a multi-day optimizer and the optimization module balances short-term and long-term goals in making such determination.

2. The system of claim 1, further comprising a position analyzer configured to estimate advertiser results on an advertisement framework.

3. The system of claim 2, wherein the position analyzer analyzes impressions and average ad positions to determine and output total impressions, bid ranks, and impression ranges to the estimation and prediction module.

4. The system of claim 1, wherein the estimation and prediction module includes a user model, the user model including a particle filter, the user model being configured to predict information about the consumer including whether the consumer is in a buying state or a browsing state.

5. The system of claim 1, wherein the estimation and prediction module includes an advertiser model, the advertiser model being configured to predict information about advertisers including how much the advertisers are bidding on keywords.

6. The system of claim 5, wherein the advertiser model includes a first estimator and a second estimator and averages modeled information from the first estimator and the second estimator to predict information about advertisers.

7. The system of claim 6, wherein the first estimator uses a particle filter and assumes joint distribution over all advertiser bids and wherein the second estimator assumes distribution over discrete bids with separate distribution for each query.

8. The system of claim 1, wherein the estimation and prediction module includes a parameter model configured to estimate unknown parameters.

9. The system of claim 1, wherein the optimization module includes a query analyzer configured to compute expected outcomes of auctions.

10. The system of claim 6, wherein the expected outcomes of auctions include numbers of clicks and conversions that may occur for a given query type.

12. The method of claim 6, wherein estimating a likelihood that a consumer will purchase is done using probabilistic filtering.

13. The method of claim 6, wherein estimating a likelihood that a consumer will purchase includes predicting whether the consumer is in a buying state or a browsing state.

14. The method of claim 6, wherein estimating a likelihood that a consumer will purchase includes predicting an amount an advertiser will bid for the keyword.

15. The method of claim 6, wherein estimating a likelihood that a consumer will purchase includes estimating unknown parameters.

16. The method of claim 6, wherein short term goals are assessed by a single-day optimizer and long term goals are assessed by a multi-day optimizer, the single-day optimizer using a greedy optimizer and the multi-day optimizer using a hill climbing search.

17. The method of claim 6, further comprising estimating advertiser results on an advertisement framework using a position analyzer.

18. The method of claim 6, wherein the modeled data comprises daily bids, ads, and spending limits for queries.

11. A method for ad auction optimization comprising:

estimating a likelihood that a consumer will purchase an advertised product, such estimating being performed by an estimation and prediction module;

using the estimated likelihood that a consumer will purchase and balancing short term and long term goals using a multi-day optimizer and a single-day optimizer of an optimization module;

determining modeled data for a keyword in an auction based on the balancing of the short term and long term goals, such determining being performed by an optimization module.

19. A computer accessible medium having stored thereon computer executable instructions, which, when executed by a computing device, operably enable the computing device to perform a procedure for ad auction optimization comprising:

estimating a likelihood that a consumer will purchase an advertised product, such estimating being performed by an estimation and prediction module;

using the estimated likelihood that a consumer will purchase and balancing short term and long term goals using a multi-day optimizer and a single-day optimizer of an optimization module;

determining modeled data for a keyword in an auction based on the balancing of the short term and long term goals, such determining being performed by an optimization module.