Abstract
In this demonstration, we present a meta-search engine based on rank-distance, which is a similarity measure between partial rankings. Our meta-search engine fetches results from four main search engines: Google, Yahoo, Bing and Ask.com and combines them into one aggregated ranking, indicating for each result the position that it had in the original rankings. This combination of multiple rankings into one that is as close as possible to them is known as the rank aggregation problem. For our search engine, we use rank-distance to solve this problem. We also give a conjecture that improves the time complexity of the existing rank aggregation algorithm based on rank-distance.
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