Keyword Generation for Search Engine Advertising

This approach leverages search engines to determine relevance between terms and captures their semantic relationships as a directed graph, which generates the common as well as the nonobvious keywords related to a term.

Abstract

Keyword generation for search engine advertising is an important problem for sponsored search or paid-placement advertising. A recent strategy in this area is bidding on nonobvious yet relevant words, which are economically more viable. Targeting many such nonobvious words lowers the advertising cost, while delivering the same click volume as expensive words. Generating the right nonobvious yet relevant keywords is a challenging task. The challenge lies in not only finding relevant words, but also in finding many such words. In this paper, we present TermsNet, a novel approach to this problem. This approach leverages search engines to determine relevance between terms and captures their semantic relationships as a directed graph. By observing the neighbors of a term in such a graph, we generate the common as well as the nonobvious keywords related to a term