Ralf Schimmel graduates on keyword suggestion

Keyword Suggestion for Search Engine Marketing

by Ralf Schimmel

Every person acquainted with the web, is also a frequent user of search engines like Yahoo and Google. Any person with a web site makes this web site with a vision in mind, most of the times this entails being found on the web. Search engines offer several methods to users that help them to be found. One group of the techniques used in this field is Search Engine Optimization (SEO), which covers everything that can be done to optimize a web site for the search engine. The whole idea of SEO is to ensure that a web site is listed in the set of search results once a matching query is entered by a user. A second important part of the search engines is Search Engine Advertisement (SEA). Billions of dollars are paid by companies that bid on keywords that match their advertisements to a users query. These keywords are hard to find, of course a company knows what it sells, but it does not know how the users search for the same products or services. Advertising in search engines can be done in multiple ways. The focus of this research lies in finding many long-tail keywords, words that often have a low search volume, but which are cheap (low competition) and which are often specific enough to ensure high conversion rates (a visitor becomes a customer). Several keyword suggestion techniques are researched and evaluated for practical use. One applicable technique is chosen, implemented and evaluated. The chosen technique is a web based technique which is using an undirected weighted graph of candidate terms (nodes), where the weight of the vertices is the semantic similarity between the two nodes, and where the term frequency of the term is stored in the node. The evaluation shows that it is a technique capable of suggesting a lot of relevant keywords that can be used for search engine marketing. According to the evaluation the technique is capable of using the term frequencies and the semantic similarities to find and rank suggestions based on popularity and relevance. The most important conclusion is that, for single term suggestions, the system outperforms Google’s suggestion system. Google’s precision on single term suggestions is better then the precision of the new tool, however the relative recall of Google is a lot worse, for both obvious and non-obvious single term suggestions. Currently the tool can only be used to complement Google’s tool, however once extended with support for multi term suggestions it can replace the entire system.

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