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：衡量职位名称之间的语义相似性是实现自动职位推荐的核心功能。该任务通常采用监督学习方法，需要以等价职位名称对的形式提供训练数据。本文则提出一种利用噪声技能标签的无监督表示学习方法，用于训练职位名称相似性模型。实验表明，该方法在文本排序与职位规范化等任务中具有显著有效性。