The Comparative Fuzzy Inference-System to Improve the Ranking of Persian Contents
Publish place: اولین مسابقه کنفرانس بین المللی جامع علوم مهندسی در ایران
Publish Year: 1395
Type: Conference paper
Language: English
View: 528
- Certificate
- I'm the author of the paper
Export:
Document National Code:
CCESI01_375
Index date: 24 January 2017
The Comparative Fuzzy Inference-System to Improve the Ranking of Persian Contents abstract
In today's world, search engines play important roles in the lives of web users, so the users are interested in search engines in order to look something up basically; therefore, they are looking for the most relevant content on the Internet. Hitherto, the various search engines has been created with the aim of meeting the needs of users; likewise, Persian users require the various search engines to meet their needs. One of the fundamental challenges of the current search engines is improving the ranking and returning relevant results to the needs of users. The present study improved the rankings of Persian contents by designing a Persian inference system. In fact, the best criterion to define an input fuzzy system improved the ranking of web pages. The effort in the present study was such that the outcomes of four top active search engines, in the field of Persian web pages, were entered into a fuzzy system as an input; then, the results of outputs of the system were compared with the preliminary results of the four search engines; this method was found to have better rankings than others in comparisons which were made with the initial results of the search engines.
The Comparative Fuzzy Inference-System to Improve the Ranking of Persian Contents Keywords:
The Comparative Fuzzy Inference-System to Improve the Ranking of Persian Contents authors
Elaheh Golzardi
Department of Computer Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran
Majid Meghdadi
University of Zanjan, Engineering Facaulty, Computer Department, Zanjan, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :