Sponsored search is a three-way interaction between advertisers, users, and the search engine. The basic ad selection in sponsored search, lets the advertiser choose the exact queries where the ad is to be shown. To increase advertising volume, many advertisers opt into advanced match, where the search engine can select additional queries that are deemed relevant for the advertiser's ad. In advanced match, the search engine is effectively bidding on the behalf of the advertisers. While advanced match has been extensively studied in the literature from the ad relevance perspective there is little work that discusses how to infer the appropriate bid value for a given advanced match. The bid value is crucial as it affects both the ad placement in revenue reordering and the amount advertisers are charged in case of a click.

Ranking Web search results has long evolved beyond simple bag-of-words retrieval models. Modern search engines routinely employ machine learning ranking that relies on exogenous relevance signals. Yet the majority of current methods still evaluate each Web page out of context. In this work, we introduce a novel source of relevance information for Web search by evaluating each page in the context of its host Web site. For this purpose, we devise two strategies for compactly representing entire Web sites. We formalize our approach by building two indices, a traditional page index and a new site index, where each "document" represents the an entire Web site. At runtime, a query is first executed against both indices, and then the final page score for a given query is produced by combining the scores of the page and its site. Experimental results carried out on a large-scale Web search test collection from a major commercial search engine confirm the proposed approach leads to consistent and significant improvements in retrieval effectiveness.