Understanding the skill sets and knowledge required for any career is of
utmost importance, but it is increasingly challenging in today's dynamic world
with rapid changes in terms of the tools and techniques used. Thus, it is
especially important to be able to accurately identify the required skill sets
for any job for better career insights and development. In this paper, we
propose and develop the Skill Recommendation (SkillRec) system for recommending
the relevant job skills required for a given job based on the job title.
SkillRec collects and identify the skill set required for a job based on the
job descriptions published by companies hiring for these roles. In addition to
the data collection and pre-processing capabilities, SkillRec also utilises
word/sentence embedding techniques for job title representation, alongside a
feed-forward neural network for job skill recommendation based on the job title
representation. Based on our preliminary experiments on a dataset of 6,000 job
titles and descriptions, SkillRec shows a promising performance in terms of
accuracy and F1-score.

本文介绍了 Skill Recommendation（SkillRec）系统，它基于职位名称，收集并确定了职位所需的技能集，并通过单词 / 句子嵌入技术和前馈神经网络为职位提供建议。在 6,000 个职位标题和描述的数据集上进行初步实验后，SkillRec 表现出很高的准确性和 F1 分数。