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The correlation of the result lists provided by search engines is fundamentaland it has deep and multidisciplinary ramifications. Here, we present automaticand unsupervised methods to assess whether or not search engines provideresults that are comparable or correlated. We have two main contributions:First, we provide evidence that for more than 80% of the input queries -independently of their frequency - the two major search engines share onlythree or fewer URLs in their search results, leading to an increasingdivergence. In this scenario (divergence), we show that even the most robustmeasures based on comparing lists is useless to apply; that is, the smallcontribution by too few common items will infer no confidence. Second, toovercome this problem, we propose the fist content-based measures - i.e.,direct comparison of the contents from search results; these measures are basedon the Jaccard ratio and distribution similarity measures (CDF measures). Weshow that they are orthogonal to each other (i.e., Jaccard and distribution)and extend the discriminative power w.r.t. list based measures. Our approachstems from the real need of comparing search-engine results, it is automaticfrom the query selection to the final evaluation and it apply to anygeographical markets, thus designed to scale and to use as first filtering ofquery selection (necessary) for supervised methods.

On the Weakenesses of Correlation Measures used for Search Engines' Results (Unsupervised Comparison of Search Engine Rankings)