Discovering context-topic rules in search engine logs

Author	dc.contributor.author	Hurtado, Carlos
Author	dc.contributor.author	Levene, Mark	es_CL
Admission date	dc.date.accessioned	2009-04-09T10:45:45Z
Available date	dc.date.available	2009-04-09T10:45:45Z
Publication date	dc.date.issued	2006
Cita de ítem	dc.identifier.citation	STRING PROCESSING AND INFORMATION RETRIEVAL, PROCEEDINGS Book Series: LECTURE NOTES IN COMPUTER SCIENCE Volume: 4209 Pages: 346-353 Published: 2006	en
Identifier	dc.identifier.issn	0302-9743
Identifier	dc.identifier.uri	https://repositorio.uchile.cl/handle/2250/124890
Abstract	dc.description.abstract	In this paper, we present a class of rules, called context-topic rules, for discovering associations between topics and contexts, where a context is defined as a set of features that can be extracted from the log file of a Web search engine. We introduce a notion of rule interesting-ness that measures the level of the interest of the topic within a context, and provide an algorithm to compute concise representations of interesting context-topic rules. Finally, we present the results of applying the methodology proposed to a large data log of a search engine.	en
Lenguage	dc.language.iso	en	en
Publisher	dc.publisher	SPRINGER-VERLAG BERLIN	en
Título	dc.title	Discovering context-topic rules in search engine logs	en
Document type	dc.type	Artículo de revista