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发布于 : Apr 24, 2019
不在本期内容中
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Apr 2019
试验
One of the challenges of search is ensuring the most relevant results for the user appear at the top of the list. This is where learning to rank (LTR) can help. LTR is the process of applying machine learning to rank documents retrieved by a search engine. If you're using Elasticsearch, you can achieve search-relevant ranking with the Elasticsearch LTR plugin. The plugin uses RankLib for generating the models during the training phase. Then, when querying Elasticsearch, you can use this plugin to "rescore" the top results. We've used it in a few projects and have been happy with the results. There's also an equivalent LTR solution for Solr users.
