Performance evaluation and comparison of the five most used search engines in retrieving web resources

Deka, Sanjib Kumar; Lahkar, Narendra

doi:10.1108/14684521011084609

Cited by 20 publications

(22 citation statements)

References 13 publications

(9 reference statements)

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“…The efficacy of the generated ranked result lists were measured via a renowned performance indicator in the IR field named TREC-Style Average Precision (TSAP). It has been widely used in the literature [7,13,16,17,26,27]. TSAP is a human-based evaluation criterion that quantifies the relevance of each generated result list considering an issued query.…”

Section: Discussionmentioning

confidence: 99%

Metasearch information fusion using linear programming

2012

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For a specific query merging the returned results from multiple search engines, in the form of a metasearch aggregation, can provide significant improvement in the quality of relevant documents. This paper suggests a minimax linear programming (LP) formulation for fusion of multiple search engines results. The paper proposes a weighting method to include the importance weights of the underlying search engines. This is a two-phase approach which in the first phase a new method for computing the importance weights of the search engines is introduced and in the second stage a minimax LP model for finding relevant search engines results is formulated. To evaluate the retrieval effectiveness of the suggested method, the 50 queries of the 2002 TREC Web track were utilized and submitted to three popular Web search engines called Ask, Bing and Google. The returned results were aggregated using two exiting approaches, three high-performance commercial Web metasearch engines and our proposed technique. The efficiency of the generated lists was measured using TREC-Style Average Precision (TSAP). The new findings demonstrate that the suggested model improved the quality of merging considerably.

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Section: Discussionmentioning

confidence: 99%

Metasearch information fusion using linear programming

2012

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“…Studies of search result retrieval for search engines and other information retrieval systems also inform database comparison methodologies. Deka and Lahkar (2010) analyzed five different search engines (Google, Yahoo!, Live, Ask, and AOL) and their abilities to produce and then reproduce relevant and unique resources, focusing on the first ten results. Shafi and Rather (2005) compared precision and recall for five search engines (AltaVista, Google, HotBot, Scirus, and Bioweb), using a series of twenty searches on biotechnology topics.…”

Section: Comparisons Of Other Information Retrieval Systemsmentioning

confidence: 99%

“…Stokes et al (2009) usedFriedman's Test to identify if there were significant differences between the databases in the precision, novelty, and availability, as well as an odds ratio test to rate if the databases were, by their definitions, effective, efficient, or accessible. Deka and Lahkar (2010) used ANOVA and Tukey's HSD tests to evaluate if the difference between the mean number of relevant/stable hits from each database were significantly higher than any other databases. Sewell (2011) also used the ANOVA test to discover any significance in the different values of precision and recall between CAB Abstracts platforms.…”

Section: Statistical Validation In Database Evaluation Studiesmentioning

confidence: 99%

“…Some studies use a point system with specified criteria to compile a total score (Shafi & Rather 2005;Stokes et al 2009;Lowe et al 2018). Other studies use simple binary relevance judgments (yes/no) made by multiple reviewers to determine a total or averaged score for each set of search results (McCain et al 1987;Deka & Lahkar 2010).…”

Section: Relevancy Calculationmentioning

confidence: 99%

“…Uniqueness is defined as the number of citations retrieved from only a single database, i.e., not present in any of the other databases (Stokes et al 2009;Deka & Lahkar 2010). For this study, the number of databases containing each citation was manually tabulated.…”

Section: Uniqueness Calculationsmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison of Selected Bibliographic Database Search Retrieval for Agricultural Information

Ritchie¹,

Banyas²,

Sevin³

2019

ISTL

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Search result retrieval was compared across eight research literature databases (AGRICOLA, AGRIS, BIOSIS, CAB Direct, FSTA, Google Scholar, Scopus, and Web of Science) for three topics from different agricultural disciplines to compare retrieval results based on searcher experience. Precision, recall, and uniqueness were analyzed by rating search results (~2400 citations) for relevancy. A generalized linear model statistical analysis determined that AGRICOLA ranked highest for precision and was statistically more likely to produce a relevant result than four other databases. CAB and Web of Science ranked highest for recall and both overlapped with AGRICOLA for statistical likelihood of producing a relevant result. Google Scholar retrieved the most unique content, but almost half of that content was not judged relevant. AGRICOLA, BIOSIS and CAB retrieved the most unique and relevant content. This study will help researchers and librarians working in the agricultural disciplines to select the bibliographic databases that will provide the most relevant search results and are most likely to meet their research need. It may also serve as a template for future bibliographic research in other disciplines.

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Evaluating the retrieval effectiveness of web search engines using a representative query sample

Lewandowski

2015

Asso for Info Science & Tech

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Search engine retrieval effectiveness studies are usually small-scale, using only limited query samples. Furthermore, queries are selected by the researchers. We address these issues by taking a random representative sample of 1,000 informational and 1,000 navigational queries from a major German search engine and comparing Google's and Bing's results based on this sample. Jurors were found through crowdsourcing, data was collected using specialised software, the Relevance Assessment Tool (RAT). We found that while Google outperforms Bing in both query types, the difference in the performance for informational queries was rather low. However, for navigational queries, Google found the correct answer in 95.3% of cases whereas Bing only found the correct answer 76.6% of the time. We conclude that search engine performance on navigational queries is of great importance, as users in this case can clearly identify queries that have returned correct results. So, performance on this query type may contribute to explaining user satisfaction with search engines.

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Performance evaluation and comparison of the five most used search engines in retrieving web resources

Cited by 20 publications

References 13 publications

Metasearch information fusion using linear programming

Metasearch information fusion using linear programming

A Comparison of Selected Bibliographic Database Search Retrieval for Agricultural Information

Evaluating the retrieval effectiveness of web search engines using a representative query sample

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