antization in Append-Only Collections

نویسندگان

  • Salman Mohammed
  • Jimmy Lin
چکیده

‹antization, the pre-calculation and conversion to integers of term/document weights in an inverted index, is a well studied aspect of search engines that substantially improves retrieval eciency. Previous work has considered the impact of quantization on e‚ectiveness–eciency tradeo‚s in retrieval, for example, exploring the relationship between collection size and quantization range in static web collections. We extend previous work to append-only collections and examine whether quantization seŠings derived from prior time periods can be applied to future time periods. Experiments con€rm that previous results generalize to a collection with di‚erent characteristics and with a di‚erent ranking function, and that in an append-only collection, we can use previous quantization seŠings in future time periods without substantial losses in either e‚ectiveness or eciency.

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تاریخ انتشار 2017