arXiv 1607.076572016-07-26cs.CL

机器学习简历-职位匹配解决方案

Machine Learned Resume-Job Matching Solution

University of Electronic Science and Technology of China（电子科技大学）

Yiou Lin, Hang Lei, Prince Clement Addo, Xiaoyu Li

摘要：

现在通过在线匹配引擎进行求职是非常突出的，对求职者和雇主都有好处。但是传统引擎的解决方案没有理解不同简历的语义含义，已经跟不上机器学习技术和计算能力令人难以置信的变化。这些解决方案通常由手动规则和预定义的关键字权重驱动，这导致低效和令人沮丧的搜索体验。为此，我们提出了一种具有丰富功能和深度学习方法的机器学习解决方案。我们的解决方案包括三个可配置的模块，可以在几乎没有限制的情况下插入。即无监督特征提取、基分类器训练和集成方法学习。在我们的解决方案中，而不是使用手动规则，机器学习的方法来自动检测位置的语义相似性。然后选择四个有竞争力的“浅”估计和“深”估计。最后，集成的方法，袋这些估计和聚合他们的个人预测，形成一个最终的预测进行了验证。对4.7万份简历的实验结果表明，该方法可以显著提高对当前职位、薪酬、学历和公司规模的预测精度。

英文摘要：

Job search through online matching engines nowadays are very prominent and beneficial to both job seekers and employers. But the solutions of traditional engines without understanding the semantic meanings of different resumes have not kept pace with the incredible changes in machine learning techniques and computing capability. These solutions are usually driven by manual rules and predefined weights of keywords which lead to an inefficient and frustrating search experience. To this end, we present a machine learned solution with rich features and deep learning methods. Our solution includes three configurable modules that can be plugged with little restrictions. Namely, unsupervised feature extraction, base classifiers training and ensemble method learning. In our solution, rather than using manual rules, machine learned methods to automatically detect the semantic similarity of positions are proposed. Then four competitive "shallow" estimators and "deep" estimators are selected. Finally, ensemble methods to bag these estimators and aggregate their individual predictions to form a final prediction are verified. Experimental results of over 47 thousand resumes show that our solution can significantly improve the predication precision current position, salary, educational background and company scale.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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英文摘要：

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