Can, F., Nuray, R., & Sevdik, A. B. (2004). Automatic performance evaluation of web search engines. Information Processing & Management, 40(3), 495-514.

Although virtually all Internet users utilize search engines to find information on the web evaluation of search engines is often difficult. A large number of searches would need to be tested and each one would need to be judged subjectively by human participants. The authors of this paper have devised a new way to test search engines and have tested their method against evaluations done by human judges, and found their automatic Web search engine evaluation method (AWSEEM) significantly predicted the subjective judgments. In the human-evaluation control, users were given a list of resources called up by the various search engines with no idea which engine each came from and were asked to rank the relevance of each. In AWSEEM, each query was run and the top 200 results for each engine were compiled into a collection of vectors which are then ranked by their similarity to the “the user information-needs” (including the question, the query, and a description of the need). The system then looks at the top 20 ranked pages for each engine and counts how many are in the top s (50 and 100 are used) commonly retrieved pages. These are assumed to be relevant.

One possible issue with this system is that it requires a little more human interaction than first assumed—the query providers must provide more than just a query. A bigger issue, though, is the choice of measure for relevancy. AWSEEM assumes that if a result appears in the results of multiple engines, it is relevant. This may be reasonable, but does raise the question—what if all the engines studied are wrong? For a simple example, searching for my own name online will retrieve a large number of results that are the same in many search engines but have nothing to do with the particular Jason Morrison who sits here typing this. Another interesting thing to note is that they did not find much of a statistically significant difference between the performance of the different search engines using either method (although more so with the human-judgment method). Very few scholarly articles (and even fewer popular press articles) bother to do this when pitting search engines against each other. Is it possible that the very notion of the “best” search engine has been statistically meaningless for some time?

The authors make a good point about the difficulty in using real users for search engine evaluation. An automated approach is one answer, but there is another—the problem is that too much time and effort is required of a small number of users. Instead, if tiny amounts of time and effort were spread across thousands or millions of users, similar results could be achieved while still using subjective measures. For example, if every time a user got results on any search engine they were presented with a simple “rate these results on a scale of 1 to 5 stars” input, they could quickly and effortlessly contribute data toward a shootout-type study. Cooperation of the search engines would not necessarily be needed, if one could use a university’s proxy to substitute or add the input for popular search engines, for example, or if a generic search page was set up to produce results from randomized (double-blind) engines. It would be interesting to try this, AWSEEM, and individual evaluation in one study to see if there was a statistical correlation.