Mining related queries from Web search engine query logs using an improved association rule mining model.
With the overwhelming volume of information, the task of finding relevant information on a given topic on the Web is becoming increasingly difficult. Web search engines hence become one of the most popular solutions available on the Web. However, it has never been easy for novice users to organize a...
| Publicado en: | Journal of the American Society for Information Science & Technology Vol. 58; no. 12; pp. 1871 - 1884 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Oct2007
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105903218&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105903218 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15322882 IGD jtl: Journal of the American Society for Information Science & Technology issn: 15322882 maglogo: Y pubinfo: dt: Oct2007 vid: 58 iid: 12 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105903218 105903218 2009684585 10.1002/asi.20632 105903218 ppf: 1871 ppct: 13 formats: tig: atl: Mining related queries from Web search engine query logs using an improved association rule mining model. aug: au: Shi X Yang CC affil: Department of Systems Engineering and Engineering Management, William M. W. Wong Engineering Building, The Chinese University of Hong Kong, Shatin, Hong Kong, People's Republic of China sug: subj: Access to Information Methods Information Retrieval Methods Internet Algorithms Data Mining Methods Funding Source Information Needs Web Search Engines Human ab: With the overwhelming volume of information, the task of finding relevant information on a given topic on the Web is becoming increasingly difficult. Web search engines hence become one of the most popular solutions available on the Web. However, it has never been easy for novice users to organize and represent their information needs using simple queries. Users have to keep modifying their input queries until they get expected results. Therefore, it is often desirable for search engines to give suggestions on related queries to users. Besides, by identifying those related queries, search engines can potentially perform optimizations on their systems, such as query expansion and file indexing. In this work we propose a method that suggests a list of related queries given an initial input query. The related queries are based in the query log of previously submitted queries by human users, which can be identified using an enhanced model of association rules. Users can utilize the suggested related queries to tune or redirect the search process. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness. Unlike many other rival techniques, it also performs reasonably well on less frequent input queries. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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