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I was wondering if there's a good way to use ensembling when I have two or more algoritims producing ranked lists. That is, suppose I have the following datasets consisting of ordered lists (higher to the top means more relevant):

Method1_Rankings  Method2_Rankings GoldStandard_Rankings
item1             item2             item1
item3             item1             item3
item2             item10            item5
...

Is there a way to optimally combine methods 1 and 2 (e.g., give the rankings some weights or similar)? Thank you.

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In this case, you might want to convert these into pair-wise relationships (e.g. item1 < item3), put together what you get from the different methods, and find a ranking which agrees with them the most.

You can look at the answer in the comment for some ideas - I would also suggest another paper that might help you: Aggregating inconsistent information: ranking and clustering

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