Bid landscape forecasting in online advertising

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.

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.