论文标题

乌龟得分 - 基于相似性的开发人员分析仪

Turtle Score -- Similarity Based Developer Analyzer

论文作者

Varshini, Sanjjushri, V, Ponshriharini, Kannan, Santhosh, Suresh, Snekha, Ramesh, Harshavardhan, Mahadevan, Rohith, Raman, Raja CSP

论文摘要

在日常生活中,对于IT公司而言,一项高度要求的任务是找到适合这些公司文化的合适候选人。这项研究旨在理解,分析和自动产生令人信服的结果,以找到一个非常适合公司的候选人。对在IT领域工作的每个员工进行了检查和收集数据,以其绩效指标。这是基于各种不同的类别来完成的,这些类别带来了多功能性和广泛的焦点。对于此数据,学习者分析是使用机器学习算法来获得学习者相似性和开发人员相似性来完成的,以招募具有相同工作模式的人。事实证明,特定工人的效率和能力在与一个类似个性的人一起工作时会更高。因此,这将是旨在招募高生产力的人的招聘人员的有用工具。这就是说,设计的模型将以高精度和完美的推荐分数提供最佳结果。

In day-to-day life, a highly demanding task for IT companies is to find the right candidates who fit the companies' culture. This research aims to comprehend, analyze and automatically produce convincing outcomes to find a candidate who perfectly fits right in the company. Data is examined and collected for each employee who works in the IT domain focusing on their performance measure. This is done based on various different categories which bring versatility and a wide view of focus. To this data, learner analysis is done using machine learning algorithms to obtain learner similarity and developer similarity in order to recruit people with identical working patterns. It's been proven that the efficiency and capability of a particular worker go higher when working with a person of a similar personality. Therefore this will serve as a useful tool for recruiters who aim to recruit people with high productivity. This is to say that the model designed will render the best outcome possible with high accuracy and an immaculate recommendation score.

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