Evaluation of web-based search engines using user-effort measures

• Main Authors: Tang, Muh-Chyun, Sun, Ying
• Format: Article
• Language: English
• Published: 2021
• Subjects: Library and information science
• Online Access: https://hdl.handle.net/10356/152490
• Institution: Nanyang Technological University

This paper presents a study of the applicability of three user-effort-sensitive evaluation measures —“first 20 full precision,” “search length,” and “rank correlation”—on four Web-based search engines (Google, AltaVista, Excite and Metacrawler). The authors argue that these measures are better alternatives than precision and recall in Web search situations because of their emphasis on the quality of ranking. Eight sets of search topics were collected from four Ph.D. students in four different disciplines (biochemistry, industrial engineering, economics, and urban planning). Each participant was asked to provide two topics along with the corresponding query terms. Their relevance and credibility judgment of the Web pages were then used to compare the performance of the search engines using these three measures. The results show consistency among these three ranking evaluation measures, more so between “first 20 full precision” and search length than between rank correlation and the other two measures. Possible reasons for rank correlation’s disagreement with the other two measures are discussed. Possible future research to improve these measures is also addressed.