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CIKM
2010
Springer

Exploiting site-level information to improve web search

8 years 9 months ago
Exploiting site-level information to improve web search
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 cons...
Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josif
Added 24 Jan 2011
Updated 24 Jan 2011
Type Journal
Year 2010
Where CIKM
Authors Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josifovski, George Mavromatis, Donald Metzler, Jane Wang
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