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Efficient query processing techniques for next-page retrieval

By Joel Mackenzie, Matthias Petri, Alistair Moffat
2021
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In top-k ranked retrieval the goal is to efficiently compute an ordered list of the highest scoring k documents according to some stipulated similarity function such as the well-known BM25 approach. In most implementation techniques a min-heap of size k is used to track the top scoring candidates. In this work we consider the question of how best to retrieve the second page of search results, given that a first page has already been computed; that is, identification of the documents at ranks k + 1 to 2k for some query. Our goal is to understand what information is available as a by-product of the first-page scoring, and how it can be employed to accelerate the secondpage computation, assuming that the second-page of results is required for only a fraction of the query load. We propose a range of simple, yet efficient, next-page retrieval techniques which are suitable for accelerating Documentat-a-Time mechanisms, and demonstrate their performance on three large text collections.
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