Personalized concept-based clustering of search engine queries
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Personalized concept-based clustering of search engine queries
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| Id. |
44848496 |
| Idioma |
inglés (Estados Unidos)
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| Titulo |
Personalized concept-based clustering of search engine queries |
| Autor(es) |
Leung, Kenneth Wai-Ting
Ng, Wilfred Siu-Hung
Lee, Dik Lun
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| Localización |
IEEE transactions on knowledge and data engineering, v. 20, no. 11, Nov. 2008, p. 1505-1518
http://hdl.handle.net/1783.1/6003
url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:IR&rft.genre=article&rft.issn=1041-4347&rft.eissn=1558-2191&rft.volume=20&rft.issue=11&rft.date=2008&rft.spage=1505&rft.epage=1518&rft.aulast=Leung&rft.aufirst=Kenneth+Wai-Ting&rft.atitle=Personalized+concept-based+clustering+of+search+engine+queries&rft.title=IEEE+transactions+on+knowledge+and+data+engineering
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| Versión |
1.0 |
| Estado |
Final
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| Descripción |
A major problem of current Web search is that search queries are usually short and ambiguous, and thus are insufficient for specifying the precise user needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user’s conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new twophase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. To the best of the authors’ knowledge, no previous work has addressed personalization for query suggestions. To evaluate the effectiveness of our technique, a Google middleware was developed for collecting clickthrough data to conduct experimental evaluation. Experimental results show that our approach has better precision and recall than the existing query clustering methods.
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| Tipo |
4322677 bytes
application/pdf
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| Palabras clave |
Clustering
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| Tipo de recurso |
Journal/magazine article
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| Tipo de Interactividad |
Expositivo
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| Nivel de Interactividad |
muy bajo
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| Audiencia |
Estudiante
Profesor
Autor
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| Estructura |
Atomic |
| Coste |
no
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| Copyright |
sí
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© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| Formatos |
4322677 bytes
application/pdf
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| Requerimientos técnicos |
Browser: Any
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| Fecha de contribución |
26-jun-2009 |
| Contacto |
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