In web search, typically a candidate generation step selects a small set of documents\u2014from collections containing as many as billions of web pages\u2014that are subsequently ranked and pruned before being presented to the user. In Bing, the candidate generation involves scanning the index using statically designed match plans that prescribe sequences of different match criteria and stopping conditions. In this work, we pose match planning as a reinforcement learning task and observe up to 20% reduction in index blocks accessed, with small or no degradation in the quality of the candidate sets.<\/p>\n","protected":false},"excerpt":{"rendered":"
In web search, typically a candidate generation step selects a small set of documents\u2014from collections containing as many as billions of web pages\u2014that are subsequently ranked and pruned before being presented to the user. In Bing, the candidate generation involves scanning the index using statically designed match plans that prescribe sequences of different match criteria 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