BID LANDSCAPE FORECASTING IN ONLINE ADVERTISING

• US 20120084142A1
• Filed: 09/30/2010
• Published: 04/05/2012
• Est. Priority Date: 09/30/2010
• Status: Abandoned Application

• Current Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Li, Wei, Mao, Jianchang, Cui, Ying Grace, Zhang, Ruofei
• Application Number:
  US12/895,773
• Publication Number:
  US 20120084142A1
• Time in Patent Office: Days
• US Class Current: 705/14.46
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0247 Calculate past, present or future revenues

Abstract

Techniques are provided for advertiser bid forecasting in online advertising, including display advertising. Methods are provided in which key targeting-related user segments are determined from bidding statistics. A feature set is extracted from an impression opportunity, based at least in part on the bidding statistics. A gradient boosting descent tree technique is utilized in determining an initial bid forecasting result. A linear regression-based model is used in post-tuning to arrive at a post-tuned result. For short-term forecasting, this may be the final result. For long-term forecasting, a hybrid approach may be utilized with further processing including utilization of a publisher-specific model.

Claims

7-1. The method of claim 1, comprising obtaining a set of user segments associated with the available future impression opportunity.

14. A system 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 historical user segment advertiser bid statistics;

based at least in part on the set of historical user segment advertiser bid statistics, determining a set of key user segments;

for an available future impression opportunity, extracting a set of features, wherein the set of features is based at least in part on the set of key user segments;

based at least in part on at least some of the set of historical user segment advertiser bid statistics, utilizing a gradient boosting descent tree technique in obtaining an initial bid forecasting result; and

based at least in part on the set of features, and based at least in part on the initial bid forecasting result, utilizing one or more linear regression-based models in performing post-tuning of the initial bid forecasting result to obtain a post-tuned bid forecasting result.

15. The system of claim 14, wherein at least one of the one or more servers is coupled to an online advertising exchange.

16. The system of claim 14, wherein the set of key user segments are user segments associated with unusually high advertiser bid amounts.

17. The system of claim 14, wherein the set of key user segments are user segments associated with at least a specified, above-average threshold.

18. The system of claim 14, wherein the set of key user segments are user segments determined to be optimized with regard to leading to an accurate post-tuned bid forecasting result.

19. The system of claim 14, wherein the system is utilized for short-term bid forecasting, and wherein short-term bid forecasting includes forecasting for periods of up to one month.

20. A computer readable medium or media containing instructions for executing a method comprising:

using one or more computers, obtaining a set of historical user segment advertiser bid statistics;

using one or more computers, based at least in part on the set of historical user segment advertiser bid statistics, determining a set of key user segments;

using one or more computers, for an available future impression opportunity, extracting a set of features, wherein the set of features is based at least in part on the set of key user segments;

using one or more computers, based at least in part on at least some of the set of historical user segment advertiser bid statistics, utilizing a gradient boosting descent tree technique in obtaining an initial bid forecasting result;

using one or more computers, based cast in part on the set of features, and based at least in part on the initial bid forecasting result, utilizing one or more linear regression-based models in performing post-tuning of the initial hid forecasting result to obtain a post-tuned bid forecasting result;

if a forecasting period being utilized is within a specified short-term threshold, then utilizing the post-tuned bid forecasting result as the final result; and

if a forecasting period being utilized is beyond a specified short-term threshold, then;

using one or more computers, for each of a set of publishers, and based at least on bidding statistics relating to each of the set of publishers, determining an associated linear regression-based publisher trend model; and

using one or more computers, based at least in part on the post-tuned bid forecasting result, utilize a publisher trend model in determining a long-term forecasting result, wherein the publisher trend model is associated with a publisher that is associated with the available future impression opportunity.