Unsupervised approach to generate informative structured snippets for job search engines
Unsupervised approach to generate informative structured snippets for job search engines
复制标题
为职位搜索引擎生成信息丰富的结构化摘要的无监督方法
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Karrie Karahalios
中科院分区：
文献类型：
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作者：
N. Spirin;Karrie Karahalios
为了改善求职搜索引擎的用户体验，本文提出了一种想法，从大多数网络搜索引擎使用的偏向查询的片段转换为与招聘页面主要部分相关联的丰富的结构化片段，由于特定的用户需求和招聘页面的结构，这些片段更适合于求职搜索。我们提供了一种非常简单但可操作的方法，以无人监督的方式生成此类代码段。建议的方法有两个优点：它不需要手动注释，因此可以轻松地部署到多种语言，这是一个国际化运作的求职搜索引擎的理想特性；它自然地与移动网络的趋势融合在一起，在移动网络中，内容需要针对小屏幕设备和信息量进行优化。
Aiming to improve user experience for a job search engine, in this paper we propose an idea to switch from query-biased snippets used by most web search engines to rich structured snippets associated with the main sections of a job posting page, which are more appropriate for job search due to specific user needs and the structure of job pages. We present a very simple yet actionable approach to generate such snippets in an unsupervised way. The advantages of the proposed approach are two-fold: it doesn't require manual annotation and therefore can be easily deployed to many languages, which is a desirable property for a job search engine operating internationally; it fuses naturally with the trend towards Mobile Web where the content needs to be optimized for small screen devices and informativeness.