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Quality-aware skill translation models for expert finding on StackOverflow

机译：质量意识技能转换模型，可在StackOverflow上找到专家

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摘要

StackOverflow has become an emerging resource for talent recognition in recent years. While users exploit technical language on StackOverflow, recruiters try to find the relevant candidates for jobs using their own terminology. This procedure implies a gap which exists between recruiters and candidates terms. Due to this gap, the state-of-the-art expert finding models cannot effectively address the expert finding problem on StackOverflow. We propose two translation models to bridge this gap. The first approach is a statistical method and the second is based on word embedding approach. Utilizing several translations for a given query during the scoring step, the result of each intermediate query is blended together to obtain the final ranking. Here, we propose a new approach which takes the quality of documents into account in scoring step. We have made several observations to visualize the effectiveness of the translation approaches and also the quality-aware scoring approach. Our experiments indicate the following: First, while statistical and word embedding translation approaches provide different translations for each query, both can considerably improve the recall. Besides, the quality-aware scoring approach can improve the precision remarkably. Finally, our best proposed method can improve the MAP measure up to 46% on average, in comparison with the state-of-the-art expert finding approach. (C) 2019 Elsevier Ltd. All rights reserved.
机译：近年来，StackOverflow已成为新兴的人才认可资源。当用户在StackOverflow上利用技术语言时，招聘人员会尝试使用自己的术语来找到相关的职位候选人。这个程序意味着招聘人员和候选人之间存在差距。由于存在这种差距，最新的专家发现模型无法有效解决StackOverflow上的专家发现问题。我们提出了两种翻译模型来弥补这一差距。第一种方法是统计方法，第二种方法是基于单词嵌入方法。在评分步骤中，使用给定查询的几种翻译，将每个中间查询的结果混合在一起以获得最终排名。在这里，我们提出了一种新的方法，该方法在计分步骤中考虑了文件的质量。我们已经进行了一些观察，以可视化翻译方法和质量意识评分方法的有效性。我们的实验表明以下几点：首先，虽然统计和词嵌入翻译方法为每个查询提供不同的翻译，但两者都可以大大提高查全率。此外，质量意识评分方法可以显着提高精度。最后，与最先进的专家发现方法相比，我们提出的最佳方法可以将MAP度量平均提高46％。 （C）2019 Elsevier Ltd.保留所有权利。

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