Measuring semantic similarity between job titles is an essential functionality for automatic job recommendations. This task is usually approached using supervised learning techniques, which requires training data in the form of equivalent job title pairs. In this paper, we instead propose an unsupervised representation learning method for training a job title similarity model using noisy skill labels. We show that it is highly effective for tasks such as text ranking and job normalization.

翻译：测量职位标题之间语义相似性是自动工作推荐的基本功能。 这个任务通常使用有监督学习技术来处理，需要以等价职位标题对的形式进行培训数据。 在本文中，我们提出了一种使用嘈杂技能标签进行职位标题相似性模型训练的无监督表示学习方法。 我们证明它对于文本排名和职位规范等任务非常有效。