ElasticSearch是一个基于Lucene的分布式实时搜索引擎解决方案。属于Elastic Stack的一部分,同时另有 logstash, kibana, beats等开源项目。

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相关搜索揭开了相关工作的神秘面纱。使用Elasticsearch,它将教会您如何将引人入胜的搜索结果返回给用户,帮助您理解并利用基于lucene的搜索引擎的内部原理。

对这项技术

用户已经习惯并期待即时的相关搜索结果。要做到这一点,你必须掌握搜索引擎。然而对于许多开发者来说,相关性排名是神秘或令人困惑的。

关于这本书

相关搜索使主题变得更清晰,并向您展示了搜索引擎是一个可编程的相关框架。您将学习如何应用Elasticsearch或Solr到您的企业独特的排名问题。该书演示了如何编写相关性程序,以及如何合并辅助数据源、分类法、文本分析和个性化。在实践中,相关性框架还需要软技能,例如与涉众协作,以发现业务的正确相关性需求。到最后,你呢?你能在搜索产品上实现一个可证明、可衡量的相关性改进的良性循环吗?

里面有什么

  • 调试技术相关性

  • 搜索引擎的特性应用到实际问题

  • 使用用户界面来指导搜索者

  • 一个系统的方法相关性

  • 专注于改善搜索的商业文化
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最新论文

Accurately linking news articles to scientific research works is a critical component in a number of applications, such as measuring the social impact of a research work and detecting inaccuracies or distortions in science news. Although the lack of links between news and literature has been a challenge in these applications, it is a relatively unexplored research problem. In this paper we designed and evaluated a new approach that consists of (1) augmenting latest named-entity recognition techniques to extract various metadata, and (2) designing a new elastic search engine that can facilitate the use of enriched metadata queries. To evaluate our approach, we constructed two datasets of paired news articles and research papers: one is used for training models to extract metadata, and the other for evaluation. Our experiments showed that the new approach performed significantly better than a baseline approach used by altmetric.com (0.89 vs 0.32 in terms of top-1 accuracy). To further demonstrate the effectiveness of the approach, we also conducted a study on 37,600 health-related press releases published on EurekAlert!, which showed that our approach was able to identify the corresponding research papers with a top-1 accuracy of at least 0.97.

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