Human Information Interaction

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Background and Aim: The aim of this study was to measure the overlap of 4 local Persian search engines of Parsijoo, Yooz, Parseek, and Rismoun and to compare the capabilities of these engines in covering indexable web.
Methods: This was an applied and evaluative research. To collect data, a keyword-based method was used. First, the selected keywords were entered into the search engines and then a sample was extracted of the retrieved records. Finally, based on the existence or absence of these records in the search engines, the necessary data were gathered. Accordingly to analyze the data, inferential statistical methods were used.
Results: The relative overlap of the Parseek compared to that of Parsijoo and Parsijoo's one compared to Yooz was 26 percent on average and Parseek had the most recall. Rismoun had not any common records with the other investigated search engines. Three search engines (Parseekc, Parsijoo and Yooz retrieved 27 common records out of 225 recalled records; there was a significant difference between the relative overlap of the 4 search engines. Also, on average, Parseel, Parsigoo, Yooz and Rismoun covered respectively 38, 31, 26, and 6 percent of the indexable web. There was a significant difference between the coverage of the 4 search engines.
Conclusion: It seems that each search engine has a different indexing policy, and users need to search for more than one search engine to get comprehensive information about an issue. It can be predicted that by foraging in two search engines, Parseek and Parsijoo, one may access 70 percent of the indexable web.

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Background and Aim: The purpose of this study was to compare the impact of text based indexing and folksonomy in image retrieval via Google search engine.
Methods: This study used experimental method. The sample is 30 images extracted from the book “Gray anatomy”. The research was carried out in 4 stages; in the first stage, images were uploaded to an “Instagram” account so the images are tagged with 600 contacts. In the second stage, the images were uploaded onto 2 blogs using text-based and folksonomy indexing, respectively. In the third stage, 118 medical experts were asked to find one of the images in Google’s image search engine. Finally, in the fourth stage, the rank of the retrieved images from the 2 blogs was reviewed.
Results: Based on the findings; in descriptive analysis, the scores of retrieved images was calculated and in the inferential analysis, independent Chi² test was used to compare the search results of two blogs. The reported difference was significant.
Conclusion: The results showed that the folksonomy improves images’ retrieval by Google search engine compared to the text-based indexing.

 

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Purpose: There are several metrics for evaluating search engines. Though, many researchers have proposed new metrics in recent years. Familiarity with new metrics is essential. So, the purpose is to provide an analysis of important and new metrics to evaluate search engines.
Methodology: This review article critically studied the efficiency of metrics of evaluation. So, “evaluation metrics,” “evaluation measure,” “search engine evaluation,” “information retrieval system evaluation,” “relevance evaluation measure” and “relevance evaluation metrics” were investigated in “MagIran” “Sid” and Google Scholar search engines. Articles gathered to inspect and analyse existing approaches in evaluation of information retrieval systems. Descriptive-analytical approach used to review the search engine assessment metrics.
Findings: Theoretical and philosophical foundations determine research methods and techniques. There are two well-known “system-oriented” and “user-oriented” approaches to evaluating information retrieval systems. So, researchers such as Sirotkin (2013) and Bama, Ahmed, & Saravanan (2015) group the precision and recall metrics in a system-oriented approach. They also believe that Average Distance, normalized discounted cumulative gain, Rank Eff and B pref are rooted in the user-oriented approach. Nowkarizi and Zeynali Tazehkandi (2019) introduced comprehensiveness metric instead of Recall metric. They argue that their metric is rooted in a user-oriented approach, while the goal is not fully met. On the other hand, Hjørland(2010) emphasizes that we need a third approach to eliminate this dichotomy. In this regard, researchers such as Borlund, Ingwersen (1998), Borlund (2003), Thornley, Gibb (2007) have mentioned a third approach for evaluating information retrieval systems that refer to interact and compose two mentioned approaches. Incidentally, Borlund, Ingwersen(1998) proposed a Jaccard Association and Cosine Association measures to evaluate information retrieval systems. It seems that these two metrics have failed to compose the system-oriented and user-oriented approaches completely,  and need further investigation.
Conclusion: Search engines involve different components including: Crawler, Indexer, Query Processor, Retrieval Software, and Ranker. Scholars  wish to apply the most efficient search engines for retrieving required information resources. Each   metrics measures a specific component, to measure all, it is suggested to select metrics from all three mentioned groups in their search.