Evaluation Evaluation of Search Engines Performance on Standardize Queries

Abubakar Kamal; Dr Sarah Bukhari; Umar Khattab; Ayesha Khan

Abubakar Kamal BZU,Multan
Dr Sarah Bukhari BZU, Multan
Umar Khattab
Ayesha Khan

Keywords: Google, Bing, Yahoo,, Baidu and DuckDuckGo, Queries

Abstract

Abstract Views: 0

The rapid expansion of web content has intensified the need for efficient and accurate search engines capable of responding effectively to diverse user queries. The study aims to compare and evaluate the performance of five major search engines (Google, Bing, Yahoo, Baidu, and DuckDuckGo) in terms of their retrieval speed and relevance across different query types: navigational, informational, and transactional. A set of standardized queries was designed, categorized, and submitted to each search engine under controlled experimental conditions. The number of relevant documents retrieved and the retrieval speed were recorded and analyzed. The results showed that Bing outperformed others in navigational queries, Google dominated informational queries, and Yahoo surprisingly excelled in transactional searches. DuckDuckGo demonstrated competitive performance, especially for navigational tasks. While, Baidu showed strength mainly in informational queries. The findings confirmed that no single search engine is superior across all query types and that search engines optimize differently depending on user intent. This research highlights the importance of query-type-specific evaluation for a better understanding of search engine performance and user satisfaction.

Downloads

Download data is not yet available.

References

I. H. Akhoon, I. Rahim, H. Mushtaq, and S. Ahmad, Evaluation of Search Engines Using Advanced Search: Comparative Analysis of Yahoo and Bing. Library Philosophy and Practice; Lincoln, 2019.

K.-L. Liu, A. Santoso, C. Yu, and W. Meng, “Discovering the representative of a search engine,” in Proc. 10th Int. Conf. Inf. Knowl. Manag. (CIKM), New York, NY, USA, Oct. 2001, pp. 577–579, doi: https://doi.org/10.1145/502585.502696.

J. Singh and S. Aditi, “A comparative study between keyword and semantic based search engines,” Paper presented at International Conference on Cloud, Big Data and Trust, Nov. 13-15, 2013.

L. Wang, J. Wang, M. Wang, Y. Li, Y. Liang, and D. Xu, “Using Internet search engines to obtain medical information: A comparative study,” J. Med. Internet Res., vol. 14, no. 3, May 2012, Art. no. 74, doi: https://doi.org/10.2196/jmir.1943.

M. Morris, J. Teevan, and K. Panovich, “A comparison of information seeking using search engines and social networks,” in Proc. Int. AAAI Conf. Web Social Media (ICWSM), May 2010, pp. 291–294, doi: https://doi.org/10.1609/icwsm.v4i1.14069.

L. Hsu and Z. Walter, “Search engine or content website? A local information seeking classification model based on consumer characteristics and website perceptions,” Int. J. Hum.-Comput. Interact., vol. 31, no. 4, pp. 263–276, Apr. 2015, doi: https://doi.org/10.1080/10447318.2014.999741.

W. Nel, L. De Wet, and R. Schall, “Randomized controlled trial of the usability of major search engines (Google, Yahoo!, and Bing) when using ambiguous search queries,” in Proc. 4th Int. Conf. Comput.-Hum. Interact. Res. Appl. (CHIRA), 2020, pp. 152–161, doi: https://doi.org/10.5220/0010133601520161.

A. Oeldorf-Hirsch, B. Hecht, M. R. Morris, J. Teevan, and D. Gergle, “To search or to ask,” in Proc. ACM Conf. Comput.-Supported Coop. Work Social Comput. (CSCW), Feb. 2014, pp. 16–27, doi: https://doi.org/10.1145/2531602.2531706.

Y. Pan, “Critical analysis of Baidu’s business strategy,” High. Bus. Econ. Manag., vol. 24, pp. 2247–2252, Jan. 2024, doi: https://doi.org/10.54097/vrhv2q88.

V. S. Parsania and F. K. K. Kamani, “A comparative analysis: DuckDuckGo vs. Google search engine,” GRD J. Eng., vol. 2, no. 1, pp. 12–17, 2016.

J. Morato, S. Sanchez-Cuadrado, and S. Navajas, “Evaluating retrieval and ranking strategies on the dark web: A focus on Tor search engines,” Inf. Discov. Deliv., Apr. 2025, doi: https://doi.org/10.1108/IDD-07-2024-0096.

S. S., S. B., S. M., S. K., K. G., and A. R., “Natural language processing techniques for information retrieval: Enhancing search engines with semantic understanding,” ITM Web Conf., vol. 76, Mar. 2025, Art. no. 05013, doi: https://doi.org/10.1051/itmconf/20257605013.

A. Giddens and E. Kuşdil, Modernliğin Sonuçları. Istanbul, Türkiye: Ayrıntı Yayınları, 2004.

C. D. Schultz, “Informational, transactional, and navigational need of information: Relevance of search intention in search engine advertising,” Inf. Retrieval J., vol. 23, no. 2, pp. 117–135, Apr. 2020, doi: https://doi.org/10.1007/s10791-019-09368-7.

S. Gul, S. Ali, and A. Hussain, “Retrieval performance of Google, Yahoo and Bing for navigational queries in the field of life science and biomedicine,” Data Technol. Appl., vol. 54, no. 2, pp. 133–150, June 2020, doi: https://doi.org/10.1108/DTA-05-2019-0083.

N. Yagci, S. Sünkler, H. Häußler, and D. Lewandowski, “A comparison of source distribution and result overlap in web search engines,” Proc. Assoc. Inf. Sci. Technol., vol. 59, no. 1, pp. 346–357, Oct. 2022, doi: https://doi.org/10.1002/pra2.758.

K. Hrabovská, B. Rossi, and T. Pitner, “Software testing process models benefits & drawbacks: A systematic literature review,” arXiv:1901.01450, Jan. 2019, doi: https://doi.org/10.48550/arXiv.1901.01450.

H. K. Azad and A. Deepak, “Query expansion techniques for information retrieval: A survey,” Inf. Process. Manag., vol. 56, no. 5, pp. 1698–1735, Sep. 2019, doi: https://doi.org/10.1016/j.ipm.2019.05.009.

S. Ali and S. Gul, “Search engine effectiveness using query classification: A study,” Online Inf. Rev., vol. 40, no. 4, pp. 515–528, Aug. 2016, doi: https://doi.org/10.1108/OIR-07-2015-0243.