Web query classification: Difference between revisions

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'''Web query topic classification/categorization''' is a problem in [[information science]]. The task is to assign a [[Web search query]] to one or more predefined [[Categorization|categories]], based on its topics. The importance of query classification is underscored by many services provided by Web search. A direct application is to provide better search result pages for users with interests of different categories. For example, the users issuing a Web query “''apple''” might expect to see Web pages related to the fruit apple, or they may prefer to see products or news related to the computer company. Online advertisement services can rely on the query classification results to promote different products more accurately. Search result pages can be grouped according to the categories predicted by a query classification algorithm. However, the computation of query classification is non-trivial. Different from the [[document classification]] tasks, queries submitted by Web search users are usually short and ambiguous; also the meanings of the queries are evolving over time. Therefore, query topic classification is much more difficult than traditional document classification tasks.

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== Difficulties ==

Web query topic classification is to automatically assign a query to some predefined categories. Different from the traditional document classification tasks, there are several major difficulties which hinder the progress of Web query understanding:

=== How to derive an appropriate feature representation for Web queries? ===

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* '''[[metasearch|Metasearch engines]]''' send a user's query to multiple search engines and blend the top results from each into one overall list. The search engine can organize the large number of Web pages in the search results, according to the potential categories of the issued query, for the convenience of Web users' navigation.

* '''[[Vertical search]]''', compared to general search, focuses on specific domains and addresses the particular information needs of niche audiences and professions. Once the search engine can predict the category of information a Web user is looking for, it can select a certain vertical search engine automatically, without forcing the user to access the vertical search engine explicitly.

* '''[[Online advertising]]'''<ref>[http://www.kdd2007.com/workshops.html#adkdd Data Mining and Audience Intelligence for Advertising (ADKDD'07)], KDD workshop 2007</ref><ref>[http://research.yahoo.com/workshops/troa-2008/ Targeting and Ranking for Online Advertising (TROA'08)], WWW workshop 2008</ref> aims at providing interesting advertisements to Web users during their search activities. The search engine can provide relevant advertising to Web users according to their interests, so that the Web users can save time and effort in research while the advertisers can reduce their advertising costs.

All these services rely on the understanding Web users' search intents through their Web queries.

== See also ==

* [[Document classification]]

* [[Web search query]]

* [[Information retrieval]]

* [[Query expansion]]

* [[Naive Bayes classifier]]

* [[Support vector machines]]

* [[Meta search]]

* [[Vertical search]]

* [[Online advertising]]

== References ==