Abstract: How can a search engine with a relatively weak relevance ranking function compete with a search engine that has a much stronger ranking function? This dual challenge, which to the best of our knowledge has not been addressed in previous work, entails an interesting bi-modal utility function for the weak search engine. That is, the goal is to produce in response to a query a document result list whose effectiveness does not fall much behind that of the strong search engine; and, which is quite different than that of the strong engine. We present a per-query algorithmic approach that leverages fundamental retrieval principles such as pseudo-feedback-based relevance modeling. We demonstrate the merits of our approach using TREC data.
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