| 其他摘要 | In light of the prevailing trends of the past three years, influenced by the pandemic and the drive for cost reduction and efficiency enhancement in enterprises, there has been a growing consideration for the integration of digital transformation into business scenarios. Particularly, the HR department, especially the recruitment function, has been significantly impacted by the environmental changes and forced transformation. The traditional recruitment methods relying on offline interviews have faced limitations, leading many companies to shift towards telephone or remote online interviews. Moreover, conventional recruitment approaches such as expert evaluations and scale analyses not only depend on past job competency experiences but are also prone to subjective evaluations and experiential biases, resulting in potential talent loss or misplacement. Recent studies have shown a correlation between individual competencies and facial actions. Additionally, machine learning techniques have a solid foundation in facial feature recognition, providing a theoretical basis for utilizing machine learning to analyze and predict competencies based on interviewees' facial features.
The main body of this study is divided into three parts. Firstly, the author analyzes the correlation between individual facial actions, current psychological states, and competencies, supported by extensive literature review in emotional psychology to verify the connection between facial actions and competencies. Secondly, the study focuses on mapping the facial action units (AUs) of the subjects during the recruitment process to the competency rating provided by experts to establish a competency level prediction model. Thirdly, the mapping relationship between the facial action units (AUs) of the subjects during the recruitment process and the competency score on the scale is examined to construct a competency score prediction model, aiming to provide a talent identification model that aligns with current societal realities and leverages machine learning technology.
The research primarily employs a series of data mining techniques and machine learning algorithm models, evaluating the predictive models against real data using various methods including grid search, parameter tuning, XGBoost tree algorithm, random forest algorithm, support vector regression (SVR), principal component analysis (PCA), singular value decomposition (SVD), five-fold cross-validation, confusion matrix, among others. The experimental results demonstrate that the XGBoost three-class rating recognition model using PCA dimensionality reduction and grid search for parameter tuning achieves an accuracy score of 0.61, indicating a promising predictive performance. The SVR competency score prediction model using PCA dimensionality reduction and grid search for parameter tuning consistently achieves an R2 performance exceeding 0.6 across ten competency dimensions. Notably, the prediction models for achievement orientation score, analytical thinking score, openness to innovation score, driving execution score, and personality potential score show an R2 value closest to 1, indicating the best fit between predicted and actual values. The Pearson correlation coefficients fall within the range of 0.7 to 0.9, indicating a strong correlation, thus demonstrating a good overall predictive scoring effect of the models. Therefore, constructing competency prediction models based on facial action units in video interviews appears to be feasible. This study embarks on an in-depth exploration of competency recognition in human resource interviews using advanced scientific technologies. Supported by a fundamental psychological theoretical framework, it holds promising commercial application prospects and expansion opportunities. |
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