The primary business model behind Web search is based on textual advertising, where contextually relevant ads are displayed alongside search results. We address the problem of selecting these ads so that they are both relevant to the queries and pro\ftable to the search engine, showing that optimizing ad relevance and revenue is not equivalent. Selecting the best ads that satisfy these constraints also naturally incurs high computational costs, and time constraints can lead to reduced relevance and pro\ftability. We propose a novel two-stage approach, which conducts most of the analysis ahead of time. An o\u000fffline preprocessing phase leverages additional knowledge that is impractical to use in real time, and rewrites frequent queries in a way that subsequently facilitates fast and accurate online matching.\u00a0 Empirical evaluation shows that our method optimized for relevance matches a state- of-the-art method while improving expected revenue. When optimizing for revenue, we see even more substantial improvements in expected revenue.<\/p>\n","protected":false},"excerpt":{"rendered":"
The primary business model behind Web search is based on textual advertising, where contextually relevant ads are displayed alongside search results. We address the problem of selecting these ads so that they are both relevant to the queries and pro\ftable to the search engine, showing that optimizing ad relevance and revenue is not equivalent. 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