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Stochastic Query Covering for Fast Approximate Document Retrieval
We design algorithms that, given a collection of documents and a distribution over user queries, return a
small subset of the document collection in such a way that we can efficiently provide high-quality answers
to user queries using only the selected subset. This approach has applications when space is a constraint
or when the query-processing time increases significantly with the size of the collection. We study our
algorithms through the lens of stochastic analysis and prove that even though they use only a small fraction
of the entire collection, they can provide answers to most user queries, achieving a performance close to the
optimal. To complement our theoretical findings, we experimentally show the versatility of our approach
by considering two important cases in the context of Web search. In the first case, we favor the retrieval of
documents that are relevant to the query, whereas in the second case we aim for document diversification.
Both the theoretical and the experimental analysis provide strong evidence of the potential value of query
covering in diverse application scenarios
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