PSO-based CNN for Keyword Selection on Google Ads

Abstract

Google Ads is a Google’s advertising system for advertisers bid on keywords in order to have their clickable ads appear in Google’s search results. The quality of the selected keywords can directly affect the advertiser's bidding cost and advertising effectiveness. However, there are a few challenges for keyword selection, as listed below. First, the number of the keywords in an ad cannot be too big due to cost constraints. Second, there could be a mixture of different language in the keywords. Third, there is the imbalance issue between ‘good’ and ‘bad’ keywords. Fourth, the effect of the typical keyword classification approach cannot produce satisfactory result. In this study, the evolutionary algorithm and deep learning are combined to deal with these challenges. By using word embedding to represent the keywords, choosing the appropriate corpus to handle the problem of mixed text with different languages, re-sampling to deal with data imbalance problem, using PSO to optimize the CNN structure and adding keyword-related features to improve classification effect, these difficulties are cleverly overcome. Finally, the keyword selection problems are successfully solved. This study is of great significance to the reduction of advertising investment cost and the increase in advertising efficiency.