Parsisanj: a semi-automatic component-based approach towards search engine evaluation
Amin Heydari Alashti, Ahmad Asgharian Rezaei, Alireza Elahi, Sobhan, Sayyaran, Mohammad Ghodsi

TL;DR
This paper introduces Parsisanj, a semi-automatic, component-based evaluation method for search engines, comparing Iranian national search engines with international ones to identify improvement pathways.
Contribution
It presents a novel semi-automatic evaluation approach specifically designed for national search engines, aiding their development and enhancement.
Findings
Iranian search engines lag behind Google and Bing in key metrics.
Component-based analysis reveals specific areas for improvement.
The evaluation method provides a clear roadmap for search engine enhancement.
Abstract
Accessing to required data on the internet is wide via search engines in the last two decades owing to the huge amount of available data and the high rate of new data is generating daily. Accordingly, search engines are encouraged to make the most valuable existing data on the web searchable. Knowing how to handle a large amount of data in each step of a search engines' procedure from crawling to indexing and ranking is just one of the challenges that a professional search engine should solve. Moreover, it should also have the best practices in handling users' traffics, state-of-the-art natural language processing tools, and should also address many other challenges on the edge of science and technology. As a result, evaluating these systems is too challenging due to the level of internal complexity they have, and is crucial for finding the improvement path of the existing system.…
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Taxonomy
TopicsWeb Data Mining and Analysis · Information Retrieval and Search Behavior · Web visibility and informetrics
