ADVERTISING BIDDING OPTIMIZATION BY TARGETING BASED ON SELF-LEARNING DATABASE

Abstract

The method of targeting advertising on Internet sites based on a structured self-learning database is considered. The database accumulates data on previously accepted requests to display ads from a closed auction, data on participation in the auction and the results of displaying ads – the presence of a click and product installation. The base is structured by streams with features – site, place, price. Each such structural stream has statistical properties that are much simpler compared to the general ad impression stream, which makes it possible to predict the effectiveness of advertising. The selection of bidding requests only promising in terms of the result allows to reduce the cost of displaying advertising.