query expansion based on relevance feedback and latent semantic analysis

نویسندگان

marziea rahimi

morteza zahedi

چکیده

web search engines are one of the most popular tools on the internet which are widely-used by expert and novice users. constructing an adequate query which represents the best specification of users’ information need to the search engine is an important concern of web users. query expansion is a way to reduce this concern and increase user satisfaction. in this paper, a new method of query expansion is introduced. this method which is a combination of relevance feedback and latent semantic analysis, finds the relative terms to the topics of user original query based on relevant documents selected by the user in relevance feedback step. the method is evaluated and compared with the rocchio relevance feedback. the results of this evaluation indicate the capability of the method to better representation of user’s information need and increasing significantly user satisfaction.

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Query expansion based on relevance feedback and latent semantic analysis

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عنوان ژورنال:
journal of ai and data mining

ناشر: shahrood university of technology

ISSN 2322-5211

دوره 2

شماره 1 2014

میزبانی شده توسط پلتفرم ابری doprax.com

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