NDLI: Categorizing web queries according to geographical locality

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Categorizing web queries according to geographical locality

Content Provider	ACM Digital Library
Author	Gravano, Luis Hatzivassiloglou, Vasileios Lichtenstein, Richard
Abstract	Web pages (and resources, in general) can be characterized according to their geographical locality. For example, a web page with general information about wildflowers could be considered a global page, likely to be of interest to a geographically broad audience. In contrast, a web page with listings on houses for sale in a specific city could be regarded as a local page, likely to be of interest only to an audience in a relatively narrow region. Similarly, some search engine queries (implicitly) target global pages, while other queries are after local pages. For example, the best results for query [wildflowers] are probably global pages about wildflowers such as the one discussed above. However, local pages that are relevant to, say, San Francisco are likely to be good matches for a query [houses for sale] that was issued by a San Francisco resident or by somebody moving to that city. Unfortunately, search engines do not analyze the geographical locality of queries and users, and hence often produce sub-optimal results. Thus query [wildflowers] might return pages that discuss wildflowers in specific U.S. states (and not general information about wildflowers), while query [houses for sale] might return pages with real estate listings for locations other than that of interest to the person who issued the query. Deciding whether an unseen query should produce mostly local or global pages---without placing this burden on the search engine users---is an important and challenging problem, because queries are often ambiguous or underspecify the information they are after. In this paper, we address this problem by first defining how to categorize queries according to their (often implicit) geographical locality. We then introduce several alternatives for automatically and efficiently categorizing queries in our scheme, using a variety of state-of-the-art machine learning tools. We report a thorough evaluation of our classifiers using a large sample of queries from a real web search engine, and conclude by discussing how our query categorization approach can help improve query result quality.
Starting Page	325
Ending Page	333
Page Count	9
File Format	PDF
ISBN	1581137230
DOI	10.1145/956863.956925
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Query classification Query modification Search engines Information retrieval Web search
Content Type	Text
Resource Type	Article

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