2017年人力资本管理数据科学国际研讨会 International Workshop on Data Science for Human Capital Management

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Robust job creation, a skilled population and engaged employees are important socioeconomic elements for the economic success and social welfare of communities. For stable labor markets, it is important to match employers with the right candidates, provide opportunities for reskilling of the labor force, and ensure that the (post-hire) workforce is engaged and productive. Human Capital Management (HCM) refers to the set of practices and systems that facilitate talent acquisition and management. It encompasses the areas of talent and labor market analytics, job advertising and distribution, professional social networks, candidate sourcing, tracking, onboarding, benefits administration and compliance.

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There are many recent successful applications of data mining and data science techniques to problems in the HCM domain:

Text classification techniques are used for job posting classification
Sequence labeling and statistical modeling approaches find application in resume and job parsing
Near-deduplication algorithms in concert with big data pipelines power many job aggregators
Predictive analytics model employee flight risk
Ontology mining techniques help build knowledge graphs of human capital entities
Personalized search and semantic search help job seekers by understanding searcher intent and contextual meaning of terms in the recruitment domain
Recommender systems have been used for expertise search and job recommendations