NEMO: Next Career Move Prediction with Contextual Embedding
NEMO: Next Career Move Prediction with Contextual Embedding
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DOI：
10.1145/3041021.3054200
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Liangyue Li;How Jing;Hanghang Tong;Jaewon Yang;Qi He;Bee-Chung Chen
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文献类型：
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作者：
Liangyue Li;How Jing;Hanghang Tong;Jaewon Yang;Qi He;Bee-Chung Chen
随着全球化和劳动力流动性的增加，人力资源在企业、行业和地区之间的重新配置已成为劳动力市场的新常态。这种流动的大量数字痕迹的出现，为以前所未有的规模和粒度了解劳动力流动提供了一个独特的机会。虽然大多数关于劳动力流动的研究主要集中在描述宏观层面（例如，地区或公司）或微观层面（例如，员工）模式，但如何准确预测员工的下一个职业变动（哪家公司拥有什么职位）的问题却很少受到关注。本文首次提出了预测未来职业变动的大规模实验研究。我们专注于预测信号的两个来源：概况上下文匹配和职业路径挖掘，并提出了一个上下文LSTM模型NEMO，通过联合学习出现在不同来源的不同类型实体（例如，员工，技能，公司）的潜在表示来同时捕获来自两个来源的信号。特别是，NEMO通过聚合所有概要信息生成上下文表示，并通过长短期记忆（LSTM）网络探索职业道路中的依赖关系。在一个大型的、真实的LinkedIn数据集上进行的大量实验表明，NEMO显著优于强基线，并揭示了微观层面劳动力流动性的有趣见解。
With increased globalization and labor mobility, human resource reallocation across firms, industries and regions has become the new norm in labor markets. The emergence of massive digital traces of such mobility offers a unique opportunity to understand labor mobility at an unprecedented scale and granularity. While most studies on labor mobility have largely focused on characterizing macro-level (e.g., region or company) or micro-level (e.g., employee) patterns, the problem of how to accurately predict an employee's next career move (which company with what job title) receives little attention. This paper presents the first study of large-scale experiments for predicting next career moves. We focus on two sources of predictive signals: profile context matching and career path mining and propose a contextual LSTM model, NEMO, to simultaneously capture signals from both sources by jointly learning latent representations for different types of entities (e.g., employees, skills, companies) that appear in different sources. In particular, NEMO generates the contextual representation by aggregating all the profile information and explores the dependencies in the career paths through the Long Short-Term Memory (LSTM) networks. Extensive experiments on a large, real-world LinkedIn dataset show that NEMO significantly outperforms strong baselines and also reveal interesting insights in micro-level labor mobility.