Ask Search Engine: Features and Performance identification

Sohrabi, Mozaffar Cheshmeh

doi:10.14704/web/v16i1/a180

Cited by 3 publications

(2 citation statements)

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“…, 2021). The study was performed on the ASK search engine to evaluate the features and precision performance (CheshmehSohrabi and Dashtaki, 2019). Pandey et al.…”

Section: Review Of Literaturementioning

confidence: 99%

Measuring performance of metasearch engines to access information: an exploratory study based on precision metrics

Bhardwaj,

Kumar,

Nazim

2024

PMM

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PurposeThis paper evaluates the precision of four metasearch engines (MSEs) – DuckDuckGo, Dogpile, Metacrawler and Startpage, to determine which metasearch engine exhibits the highest level of precision and to identify the metasearch engine that is most likely to return the most relevant search results.Design/methodology/approachThe research is divided into two parts: the first phase involves four queries categorized into two segments (4-Q-2-S), while the second phase includes six queries divided into three segments (6-Q-3-S). These queries vary in complexity, falling into three types: simple, phrase and complex. The precision, average precision and the presence of duplicates across all the evaluated metasearch engines are determined.FindingsThe study clearly demonstrated that Startpage returned the most relevant results and achieved the highest precision (0.98) among the four MSEs. Conversely, DuckDuckGo exhibited consistent performance across both phases of the study.Research limitations/implicationsThe study only evaluated four metasearch engines, which may not be representative of all available metasearch engines. Additionally, a limited number of queries were used, which may not be sufficient to generalize the findings to all types of queries.Practical implicationsThe findings of this study can be valuable for accreditation agencies in managing duplicates, improving their search capabilities and obtaining more relevant and precise results. These findings can also assist users in selecting the best metasearch engine based on precision rather than interface.Originality/valueThe study is the first of its kind which evaluates the four metasearch engines. No similar study has been conducted in the past to measure the performance of metasearch engines.

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“…, 2021). The study was performed on the ASK search engine to evaluate the features and precision performance (CheshmehSohrabi and Dashtaki, 2019). Pandey et al.…”

Section: Review Of Literaturementioning

confidence: 99%

Measuring performance of metasearch engines to access information: an exploratory study based on precision metrics

Bhardwaj,

Kumar,

Nazim

2024

PMM

View full text Add to dashboard Cite

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“…They found that Yahoo Image Search has better performance than two others in both two measures. CheshmehSohrabi and Abassi-Dashtaki (2019) evaluated the Ask search engine performance based on the three types of keyword, phrase, and question queries using recall and precision measures. The results indicate an average of less than 50% for recall and precision, respectively.…”

Section: Related Researchmentioning

confidence: 99%

Proposing a New Combined Indicator for Measuring Search Engine Performance and Evaluating Google, Yahoo, DuckDuckGo, and Bing Search Engines based on Combined Indicator

Hoseinabadi

CheshmehSohrabi

2022

Journal of Librarianship and Information Science

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This study has developed a combined indicator to evaluate the performance of different search engines. Documentary analysis, survey, and evaluative methods are employed in the present study. The research was conducted in two stages. First, a combined indicator was designed to measure search engines. To this end, 72 criteria for measuring the performance of search engines were identified, out of which 22 criteria were selected. Accordingly, 10 criteria were selected in six general classes through a survey of subject matter experts. Validation of our proposed combined indicator was obtained by Delphi method and using the opinions of experts in the fields of information science and information system. Second, web search engines were evaluated based on the proposed combined indicator. The statistical population of this part of the research consisted of two categories: (1) general web search engines, and (2) general subjects. The sample size of the first category contained four search engines Yahoo, Google, DuckDuckGo, and Bing, and the second category involved 40 search terms under 10 general categories. The results showed that the combined indicator had six general criteria: (1) relevance, (2) ranking, (3) novelty ratio, (4) coverage ratio, (5) ratio of unrelated documents, and (6) proportion of duplication hits. According to this indicator, Google is at the top, followed by Bing. This study proposes a new indicator for evaluating search engine performance, which can measure the efficiency of search engines. Therefore, its use to measure the performance of search engines is recommended to researchers and search engine developers.

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