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发布于 : Apr 24, 2019
不在本期内容中
这一条目不在当前版本的技术雷达中。如果它出现在最近几期中，那么它很有可能仍然具有相关参考价值。如果这一条目出现在更早的雷达中，那么它很有可能已经不再具有相关性，我们的评估将不再适用于当下。很遗憾我们没有足够的带宽来持续评估以往的雷达内容。
了解更多
Apr 2019
Trial
值得一试。了解为何要构建这一能力是很重要的。企业应当在风险可控的前提下在项目中尝试应用此项技术。
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.
