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Human-Explainable Features for Job Candidate Screening Prediction

机译：求职筛选预测的人为可解释的特征

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摘要

Video blogs (vlogs) are a popular media form for people to present themselves. In case a vlogger would be a job candidate, vlog content can be useful for automatically assessing the candidates traits, as well as potential interview ability. Using a dataset from the CVPR ChaLearn competition, we build a model predicting Big Five personality trait scores and interview ability of vloggers, explicitly targeting explainability of the system output to humans without technical background. We use human-explainable features as input, and a linear model for the systems building blocks. Four multimodal feature representations are constructed to capture facial expression, movement, and linguistic usage. For each, PCA is used for dimensionality reduction and simple linear regression for the predictive model. Our system's accuracy lies in the middle of the quantitative competition chart, while we can trace back the reasoning behind each score and generate a qualitative analysis report per video.
机译：视频博客（VLOGS）是一个流行的媒体形式，供人们呈现自己。如果vlogger是作业候选人，VLOG内容对于自动评估候选性特征以及潜在的面试能力有用。使用来自CVPR Chalearn竞赛的数据集，我们构建了一个模型预测五个人格特质分数和vloggers的面试能力，明确地定位系统输出的解释性，没有技术背景。我们使用人的可解释功能作为输入，以及系统构建块的线性模型。构建四种多模式特征表示，以捕获面部表情，运动和语言使用。对于每个，PCA用于预测模型的维度降低和简单的线性回归。我们的系统的准确性位于定量竞争表的中间，而我们可以追溯每个分数后面的推理并生成每个视频的定性分析报告。

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