Volume 7, Issue 2 (12-2020)
Human Information Interaction 2020, 7(2): 1-15 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
sanatjoo A, Zeynali Tazehkandi M. Review of Ranked and Unranked-based Metrics for Determining the Effectiveness of Search Engines. Human Information Interaction 2020; 7 (2)
URL: http://hii.khu.ac.ir/article-1-2911-en.html
URL: http://hii.khu.ac.ir/article-1-2911-en.html
Abstract: (4898 Views)
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.
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.
Keywords: Information Retrieval System, Search Engine, Relevance Evaluation, Assessment Measures, Effectiveness Metric.
References
1. Abbasi Dashtaki, N; Cheshmeh Sohrabi, M (2019). Google, Yahoo and Bing Search Engines' Performance in the Persian Information Retrieval: A Fuzzy and Classical Evaluation. Journal of National Studies on Librarianship and Information Organization ,30(2),96-111. (Persian)
2. Al-Maskari, A., Sanderson, M., & Clough, P. (2007, July). The relationship between IR effectiveness measures and user satisfaction. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 773-774). [DOI:10.1145/1277741.1277902]
3. Asadi Qadikolaei O., Asadi S., Noroozi A., Ehsani R, (2014). A Comparison of Precision in General Search Engines and Specialized Databases for Radiology Image Retrieval, Journal of Health Management, 5(2), 77-87. (Persian)
4. Azadi, Gh (2005). The scale of web search engines precision in information retrieval of library and information science discipline, National Studies on Librarianship and Informaion Organization, 16(3), 111. (Persian)
5. Babaei, E and Sajedi, M (2013). A Comparative Study on Efficiency of Medical Specialized Search Engines in Retrieving Information Related to Gynecology and Obstetrics, Health Information Management, 10(2), 234. (Persian)
6. Bama, S. S., Ahmed, M. I., & Saravanan, A. (2015). A survey on performance evaluation measures for information retrieval system. International Research Journal of Engineering and Technology, 2(2), 1015-1020.
7. Bar-Ilan, J. (1998). On the overlap, the precision and estimated recall of search engines. A case study of the query "Erdos". Scientometrics, 42(2), 207-228. [DOI:10.1007/BF02458356]
8. Bayanvand, A (2012). The Basics of Computer and Internet.Tehran: Chapar.( Persian)
9. Bilal, D. (2012). Ranking, relevance judgment, and precision of information retrieval on children's queries: Evaluation of Google, Y ahoo!, B ing, Y ahoo! K ids, and ask K ids. Journal of the American Society for Information Science and Technology, 63(9), 1879-1896. [DOI:10.1002/asi.22675]
10. Borlund, P. & Ingwersen, P. (1998) Measures of relative relevance and ranked half-life: performance indicators for interactive IR. In: Croft, B.W, Moffat, A., van Rijsbergen, C.J., Wilkinson, R., and Zobel, J., eds. [DOI:10.1145/290941.291019]
11. Borlund, P (2003). The IIR evaluation model: a framework for evaluation of interactive information retrieval systems. Information Research, 8(3). [Available at: http://informationr.net/ir/8-3/paper152.html]
12. Buckley, C., & Voorhees, E. M. (2004, July). Retrieval evaluation with incomplete information. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 25-32). ACM. [DOI:10.1145/1008992.1009000]
13. Budd, J M (2004). Relevance: Language, semantics, philosophy. Library trend, 52(3).
14. Büttcher, S., Clarke, C. L., & Cormack, G. V. (2016). Information retrieval: Implementing and evaluating search engines. Mit Press.
15. Clarke, S. J., & Willett, P. (1997). Estimating the recall performance of Web search engines. Aslib Proceedings, 49 (7), 184-189. [DOI:10.1108/eb051463]
16. Comperhensive list of search engines (2017), In The search engine list website, Retrivited 9 Dec. 2017 from http://www.thesearchenginelist.com/
17. Croft, W. B., Metzler, D., & Strohman, T. (2015). Search engines: Information retrieval in Practice. London: Pearson Education.
18. Daverpanah, MR (2004). Paradigm and information retrieval. Iranian Journal of Library and Information Science, 7(3), 2-14.( Persian)
19. Mea, D; Gianluca,V; Luca, D; Gaspero, D and Mizzaro, S. (2006) Measuring retrieval effectiveness with average distance measure (ADM). Information Wissenschaft und Praxis 57( 8), 433-443.
20. Demirci, R. G., Kismir, V. and Bitirim, Y. (2007), An evaluation of popular search engines on finding turkish document, Second International Conference on Internet and Web Applications and Services (ICIW'07), IEEE, Turkey, pp.1-5. [DOI:10.1109/ICIW.2007.15] [PMID]
21. Thornley, C. and Gibb, F. (2007). A dialectical approach to information retrieval. Journal of documentation, 63 (5), 755-764. [DOI:10.1108/00220410710827781]
22. Fidel, R. (2008). Are we there yet? Mixed methods research in library and information science. Library & Information Science Research, 30(4), 265-272. [DOI:10.1016/j.lisr.2008.04.001]
23. Frické, M. (1998), "Measuring recall", Journal of information science,24 (6), 409-417. [DOI:10.1177/016555159802400604]
24. Garoufallou, E. (2012). Evaluating search engines: A comparative study between international and Greek SE by Greek librarians. Program: electronic library & information systems, 46(2), 182-198. [DOI:10.1108/00330331211221837]
25. Ghiasi, M; Daliri, S; Kouchakinejad, L and Abbasian Joushghani, A (2015). A Comparison of Accuracy in Specialized Medical Search and General Search Engines for Retrieving Medical Image, Educational Developement of Jundishapur, 6(2), 131-138. (Persian)
26. Goel, S., & Yadav, S. (2012). An Overview of Search Engine Evaluation Strategies. International Journal of Applied Information Systems, 1, 7-10. [DOI:10.5120/ijais12-450156]
27. Hariri N, Babalhavaeji F, Farzandipour M, Nadi Ravandi S(2014). Evaluation Criteria of Information Retrieval Systems: What We Know and What We Do Not Know.; Iranian Research Institute for Information Science and Technology, 30 (1):199-221.( Persian)
28. Hariri, N and Vakili Mofrad, H (2014). A Comparison of the Precision of General and Specialized Medical Search Engines in Medical Images Retrieval, Health Information Management, 10(6), 830-839. (Persian)
29. Hariri, N. (2011). Relevance ranking on Google. Online Information Review. 35(4), 598-610. [DOI:10.1108/14684521111161954]
30. Henzinger, M. (2007). Search technologies for the Internet. Science, 317(5837), 468-471. [DOI:10.1126/science.1126557] [PMID]
31. Hjørland, B. (2010). The foundation of the concept of relevance. Journal of the American Society for Information Science and Technology, 61 (2), 217-237. [DOI:10.1002/asi.21261]
32. Ingwersen, P. (2010). Information retrieval interaction. Translated by Hajar Setodeh.Tehran: Ketabdar.
33. Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS), 20(4), 422-446. [DOI:10.1145/582415.582418]
34. Kent, A., Berry, M. M., Luehrs Jr, F. U., and Perry, J. W. (1955), "Operational criteria for designing information retrieval systems", American documentation, 6 (2), 93-101. [DOI:10.1002/asi.5090060209]
35. Kosha, K (2002). Internet Exploration Tools: Search Principles, Skills, and Features. Tehran: Ketabdar. (Persian).
36. Kumar, B. S., & Prakash, J. N. (2009). Precision and relative recall of search engines: A comparative study of Google and Yahoo. Singapore Journal of Library & Information Management, 38(1), 124-13
37. Kumar, B. T., and Sampath Pavithra, S. M. (2010), Evaluating the searching capabilities of search engines and metasearch engines: a comparative study, Annals of Library and Information Studies), 57 (2), 87-97.
38. Kumar, K. and Bhadu, V. (2013), A comparative study of BYG search engines, American Journal of engineering research, 2 (4), 39-43.
39. Lancaster, F. W. (1979), Information retrieval systems; characteristics, testing, and evaluation (2nd ed ed.), Wiley, New York..
40. Landoni, M. and Bell, S. (2000), Information retrieval techniques for evaluating search engines: a critical overview, Aslib Proceedings, 52 (3), 124-129. [DOI:10.1108/EUM0000000007006]
41. Lewandowski, D. (Ed.). (2012). Web search engine research. Emerald Group Publishing Limited. [DOI:10.1108/S1876-0562(2012)4]
42. Lopes, C. T., and Ribeiro, C. (2011). Comparative evaluation of web search engines in health information retrieval. Online Information Review, 35(6), 869-892. [DOI:10.1108/14684521111193175]
43. Mea, V. D., & Mizzaro, S. (2004). Measuring retrieval effectiveness: A new proposal and a first experimental validation. Journal of the American Society for Information Science and Technology, 55(6), 530-543. [DOI:10.1002/asi.10408]
44. Mizzaro, S. (2001, September). A new measure of retrieval effectiveness (or: What's wrong with precision and recall). In International workshop on information retrieval (IR'2001) (pp. 43-52). Infotech Oulu.
45. Mohammad Esmaeil, S and Mansour Kiaie, R (2012). A Comparison between Search Engines and Meta-search Engines in Retrieving Information Related to Physics and the Extent of their Overlap, National Studies on Librarianship and Informaion Organization, 22(3), 130. (Persian)
46. Mohammadesmaeil, S; Lafzighazi, E and Gilvari, A (2008). Comparing Search Engines and Meta-search Engines in Pharmaceutics Information Retrieval, Health Information Management, 5(2), 121-129. (Persian)
47. Mohammadesmeil, S and Naraghian, N (2017). Comparing Search Engines and Meta Search Engines in Dentistry Information Retrieval, Journal of Research in Dental Sciences, 14(2), 118-127. (Persian)
48. Nowkarizi, M; Zeynali Tazehkandi, M. (2019). Rethinking the Recall Measure in Appraising Information Retrieval Systems and Providing a New Measure by Using Persian Search Engines. International Journal of Information Science and Management,17(1), 1-16.
49. Nowkarizi,M and Zeynali Tazehkandi, M (2017). The overlap and coverage of 4 local search engines: Parsijoo, Yooz, Parseek and Rismoun, Human Information Interaction, 4(3), 48-59. (Persian)
50. Pao, M. L. (2000). Concepts of information retrieval. Translated by Asad Olah Azad and RahmattolahFattahi.Mashhad: Ferdowsi university of Mashhad. (Persian)
51. Powers, D.M.W., 2011. Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation. Journal of Machine LearningTechnologies, 2(1), 37-63.
52. Rajabi,M and Norouzi, Y (2015). Persian Search Engines: Evaluating Search Features, Information Retrieval, Precision and Recall and Their Overlaps, National Studies on Librarianship and Informaion Organization, 26(3), 133-150. (Persian)
53. Riahinia N, Rahimi F and AllahBakhshian L (2015). Matching Scores of System Relevance and User-Oriented Relevance in SID, ISC and Google Scholar. Human Information Interaction, 2 (1), 1-11. (Persian)
54. Sakai, T. (2007, July). Alternatives to bpref. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 71-78). ACM. [DOI:10.1145/1277741.1277756]
55. Sakai, T. (2012, April). Evaluation with informational and navigational intents. In Proceedings of the 21st international conference on World Wide Web (pp. 499-508). [DOI:10.1145/2187836.2187904]
56. Sakai, T., & Kando, N. (2008). On information retrieval metrics designed for evaluation with incomplete relevance assessments. Information Retrieval, 11(5), 447-470. [DOI:10.1007/s10791-008-9059-7]
57. Sakai, T., & Song, R. (2011, July). Evaluating diversified search results using per-intent graded relevance. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (pp. 1043-1052). [DOI:10.1145/2009916.2010055] [PMID]
58. Saracevic, T. (2007). Relevance: A Review of the Literature and a Framework for Thinking on the Notion in Information Science: Nature and Manifestations of Relevance. Journal of the American Society for Information Science and Technology, 58 (13), 1915-1933. [DOI:10.1002/asi.20682]
59. Saracevic, T. (2015). Why is relevance still the basic notion in information science. In Re: inventing Information Science in the Networked Society. Proceedings of the 14th International Symposium on Information Science (ISI 2015) (pp. 26-36).
60. Shafi, S. and Rather, R. A. (2005), "Precision and recall of five search engines for retrieval of scholarly information in the field of biotechnology", Webology, 2 (2), 42-47.
61. Shang, Y., and Li, L. (2002). Precision evaluation of search engines. World Wide Web, 5(2), 159-173. [DOI:10.1023/A:1019679624079]
62. Sirotkin, P. (2012). On Search Engine Evaluation Metrics. arXiv preprint arXiv:1302.2318.
63. Smith, A. G. (2003). Think local, search global? Comparing search engines for searching geographically specific information. Online Information Review., 27(2), 102-109. [DOI:10.1108/14684520310471716]
64. Soleymani, H (2009).Web search and database training. Tehran: Hojatolah Soleymani
65. Vaughan, L. (2004). New measurements for search engine evaluation proposed and tested. Information Processing & Management, 40(4), 677-691. [DOI:10.1016/S0306-4573(03)00043-8]
66. Yilmaz, E., Carterette, B., & Kanoulas, E (2012). Evaluating Web Retrieval Effectiveness. In Dirk lewandowski, web search engine research. Bingley, west Yorkshire: Emerald Group Publishing Limited. [DOI:10.1108/S1876-0562(2012)002012a007]
67. Yosefi, A (1997). False drop in information storage and retrieval. Iranian Journal of Scientific Information and Documentation Center, 13(1), 1-9.( Persian).
68. Zeynali Tazehkandi, M. and Nowkarizi, M. (2020). Evaluating the effectiveness of Google, Parsijoo, Rismoon, and Yooz to retrieve Persian documents. Library Hi Tech.
https://doi.org/10.1108/LHT-11-2019-0229 [DOI:10.1108/LHT-11-2019-0229.]
69. Zhou, B., & Yao, Y. (2010). Evaluating information retrieval system performance based on user preference. Journal of Intelligent Information Systems, 34(3), 227-248.Zuva, K. and Zuva, T. (2012), "Evaluation of information retrieval systems", International journal of computer science and information technology, 4 (3),35-43. [DOI:10.1007/s10844-009-0096-5]
70. Zuva, K., and Zuva, T. (2012). Evaluation of information retrieval systems. International journal of computer science & information technology, 4(3), 35-43. [DOI:10.5121/ijcsit.2012.4304]
71. Croft, W. B., Metzler, D., & Strohman, T. (2015). Search engines. In Information Retrieval in Practice. Pearson Education, Inc.
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
