基于DISC理论分类和神经网络的自动兼容性预测引擎
An Automated Compatibility Prediction Engine using DISC Theory Based Classification and Neural Networks
摘要：
传统上，心理测量测试被用来分析新来的工人。这些方法使用DISC分析方法将人分为不同的人格类型，这些类型进一步用于预测一个人是否可能适合组织文化。通过引入一种新的技术来预测一对特定的即将到来的工人和被分配的经理在心理尺度上是否相容，进一步扩展了这一概念。这是使用多层感知器神经网络，它可以自适应地训练，以展示兼容性指数的真实性质。所提出的原型模型用于量化相关属性，使用它们来训练预测引擎，并定义所需的数据管道。
英文摘要：
Traditionally psychometric tests were used for profiling incoming workers. These methods use DISC profiling method to classify people into distinct personality types, which are further used to predict if a person may be a possible fit to the organizational culture. This concept is taken further by introducing a novel technique to predict if a particular pair of an incoming worker and the manager being assigned are compatible at a psychological scale. This is done using multilayer perceptron neural network which can be adaptively trained to showcase the true nature of the compatibility index. The proposed prototype model is used to quantify the relevant attributes, use them to train the prediction engine, and to define the data pipeline required for it.
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。
