Query expansion based on relevance feedback and latent semantic analysis | ||
| Journal of AI and Data Mining | ||
| مقاله 22، دوره 2، شماره 1، خرداد 2014، صفحه 79-84 اصل مقاله (341.97 K) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2014.188 | ||
| نویسندگان | ||
| Marziea Rahimi* 1؛ Morteza Zahedi2 | ||
| 1Shahrood University of Technology | ||
| 2School of IT and computer engineering- Shahrood University of Technology | ||
| 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. | ||
| کلیدواژهها | ||
| Query expansion؛ latent semantic analysis؛ relevant feedback | ||
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