Study Of Search Engine Advertising Keyword Optimization

Currently, search engine advertising is the major delivering manner and the effective marketing method of network advertising, advertisers exhibit their services and products in order to obtain economic benefits through their delivered advertisements, meanwhile the search engine users read the advertisements and advertising information through the matching between the search keywords they input and the bidding advertising keywords they purchase. Whether the ads can be discovered accurately by users who are interested in them and obtain more chances to exhibit to users depends on the keyword optimization for search engine advertising. At present, a common demand for advertiser is to obtain plenty of keywords automatically which are related to a specific advertisement and can bring more economic benefits. The related issues corresponding to this demand is search engine advertising keywords optimization. Search engine advertising keywords optimization is a hot topic and a difficult point in the research area of search engine advertising, its difficulty is how to generate a group of bidding keywords which contains a mass of keywords which are related to the advertisements delivered by advertisers and can help advertisers obtain more economic benefits.In order to solve the difficulty existing in the area of Search engine advertising keyword optimization, we argue that advertising keyword optimization can be divided into three stages to process. The first stage is advertising keyword extraction. The main task of this stage is to design the model of advertising keyword extraction and extract keywords as the seed keywords from advertisement. We apply an extraction model based on language pattern mining. This model can guarantee that the seed keywords have a very high relevance with advertisement. The second stage is advertising keyword expansion. The main task of this stage is to design the model of advertising keyword expansion and generate plenty of candidate bidding keywords which are related to seed keywords. We apply a expansion model based on concept hierarchy, this model can guarantee the large number of the candidate bidding keywords and the high relevance with the seed keywords. The third stage is candidate bidding keywords optimization and selection. The main task of this stage is to design the model of optimization to select more superior keywords from candidate bidding keywords. We apply a optimization model based on click-through rate prediction. This model can guarantee that the optimal results can bring greater economic benefits for the advertisers.Based on the above works, we make the experiment to evaluate the effect of the search engine advertising keyword optimization method which is composed of the above three models. Firstly, we verify that the keyword extraction model based on language pattern mining is better than traditional keyword extraction model. Secondly, we verify that click-through rate optimization model base on LRM has a high precision on experimental result. These two experimental results provide very big support to the effect evaluation of entire optimization algorithm. Finally, we implement an experiment to compare the search engine advertising keyword optimization method with leading advertising keyword suggestion tools. The result shows that our keyword optimization method is better than leading advertising keyword suggestion tools.