SYSTEM AND METHOD FOR CONTEXT-ADAPTIVE SHAPING OF RELEVANCE SCORES FOR POSITION AUCTIONS

• US 20090138362A1
• Filed: 01/31/2009
• Published: 05/28/2009
• Est. Priority Date: 06/08/2007
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Schroedl, Stefan, Kesari, Anandsudhakar
• Application Number:
  US12/363,756
• Publication Number:
  US 20090138362A1
• Time in Patent Office: Days
• US Class Current: 705/14.550
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0257 User requested
• CPC Class Codes: G06Q 30/0263 based upon Internet or website rating
• CPC Class Codes: G06Q 30/0275 Auctions
• CPC Class Codes: G06Q 30/0277 Online advertisement
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

The present invention is directed towards systems and methods for ranking and providing advertisements in a position auction. The method of the present invention comprises receiving a search query and selecting at least one keyword based upon the search query. A list containing at least one keyword based upon the search query is returned and a list comprising at least one bid corresponding to the returned list of keywords is retrieved. The search query and list comprising at least one bid are used to train an offline simulator. The offline simulator creates a model that predicts optimal scoring factors. A priority score corresponding to each bid is computed using the optimal scoring factors and used to rank the list of bids. Advertisements are then provided corresponding to a plurality of the highest ranking bids.

Claims

1. A system for ranking and providing advertisements in a position auction, the system comprising:

an offline simulator operable to,

receive a set of queries,

receive a corresponding set of advertisements to the queries,

compute a training scoring factor,

analyze the training scoring factor, and

generate a model operable to predict one or more optimal scoring factors; and

a rank generator operable to,

compute a priority score corresponding to an advertisement associated with a given bid comprising an optimal scoring factor predicted by the model, and

rank the advertisement according to the priority score.

2. The system according to claim 1 wherein the offline simulator is operative to compute the training scoring factor to meet a business metric criterion.

3. The system according to claim 2 wherein the business metric criteria comprises revenue per search.

4. The system according to claim 1 wherein the offline simulator is operative to compute the training scoring factor to meet a plurality of business metric criteria.

5. The system according to claim 4 wherein the offline simulator is operative to compute the training scoring factor to apply a weighted combination of the plurality of business metric criteria.

6. The system according to claim 4 wherein the plurality of business metric criteria comprises revenue per search, click through rate and price per click.

7. The system according to claim 1 wherein the optimal scoring factor is a predictive replication of the training scoring factor.

8. The system according to claim 1 wherein the offline simulator is operative to retrieve the set of queries and the corresponding set of advertisements to the queries at random intervals of time.

9. The system according to claim 1 wherein the offline simulator is operative to retrieve the set of queries and the corresponding set of advertisements to the queries at periodic intervals of time.

10. The system according to claim 1 wherein the offline simulator is operative to compute the training scoring factor by selecting values between an upper bound and a lower bound.

11. A method for ranking advertisements in a position auction comprising:

receiving a set of search queries;

receiving a corresponding set of advertisements to the queries;

computing a training scoring factor;

analyzing the training scoring factor;

generating a model operable to predict one or more optimal scoring factors;

computing a priority score corresponding to an advertisement associated with a given bid, the priority score comprising an optimal scoring factor predicted by the model; and

ranking the advertisement according to the priority score.

12. The method according to claim 11 wherein the training scoring factor is computed to meet a business metric criterion.

13. The method according to claim 12 wherein the business metric criteria comprises revenue per search.

14. The method according to claim 11 wherein the training scoring factor is computed to meet a plurality of business metric criteria.

15. The method according to claim 14 wherein the training scoring factor is computed to apply a weighted combination of the plurality of business metric criteria.

16. The method according to claim 14 wherein the plurality of business metric criteria comprises revenue per search, click through rate and price per click.

17. The method according to claim 11 wherein the optimal scoring factor is a predictive replication of the training scoring factor.

18. The method according to claim 11 wherein the set of queries and the corresponding set of advertisements to the queries are retrieved at random intervals of time.

19. The method according to claim 11 wherein the set of queries and the corresponding set of advertisements to the queries are retrieved at periodic intervals of time.

20. The method according to claim 11 wherein the training scoring factor is computed by selecting values between an upper bound and a lower bound.