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    copied!<p>This is normally done by creating a test suite of questions and then evaluating how well the search response answers those questions. In some cases the responses should be unambiguous (if you type <code>slashdot</code> into a search engine you expect to get <code>slashdot.org</code> as your top hit), so you can think of these as a class of hard queries with 'correct' answers.</p> <p>Most other queries are inherently subjective. To minimise bias you should ask multiple users to try your search engine and rate the results for comparison with the original. Here is an example of a computer science paper that does something similar:</p> <p><a href="http://www.cs.uic.edu/~liub/searchEval/SearchEngineEvaluation.htm" rel="noreferrer">http://www.cs.uic.edu/~liub/searchEval/SearchEngineEvaluation.htm</a></p> <p>Regarding specific comparison of the algorithms, although obvious, what you measure depends on what you're interested in knowing. For example, you can compare efficiency in computation, memory usage, crawling overhead or time to return results. If you are trying to produce very specific behaviour, such as running specialist searches (e.g. a literature search) for certain parameters, then you need to explicitly test this. </p> <p>Heuristics for relevance are also a useful check. For example, when someone uses search terms that are probably 'programming-related', do you tend to get more results from <code>stackoverflow.com</code>? Would your search results be better if you did? If you are providing a set of trust weightings for specific sites or domains (e.g. rating <code>.edu</code> or <code>.ac.uk</code> domains as more trustworthy for technical results), then you need to test the effectiveness of these weightings.</p>
 

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