标题： 基于相似图集成的分层作业分类 显示英文标题

标题： Hierarchical Job Classification with Similarity Graph Integration

摘要： 在在线招聘的动态领域中，准确的工作分类对于优化工作推荐系统、搜索排名和劳动力市场分析至关重要。 随着就业市场的演变，职位名称和描述的复杂性不断增加，这需要能够有效利用职位数据中复杂关系的先进模型。 传统的文本分类方法常常不足，特别是由于它们无法充分利用行业分类的层次结构。 为了解决这些限制，我们提出了一种新颖的表示学习和分类模型，将工作和层次化的行业类别嵌入到一个潜在的嵌入空间中。 我们的模型整合了标准职业分类（SOC）系统和内部的层次分类法Carotene，以捕捉图和层次关系，从而提高分类准确性。 通过将层次化的行业类别嵌入到共享的潜在空间中，我们解决了冷启动问题，并增强了候选人与工作机会的动态匹配。 在大规模职位发布数据集上的广泛实验表明，该模型在利用层次结构和丰富的语义特征方面表现出色，显著优于现有方法。 这项研究提供了一个强大的框架，用于提高工作分类的准确性，支持招聘行业中更明智的决策。 显示英文摘要

摘要： In the dynamic realm of online recruitment, accurate job classification is paramount for optimizing job recommendation systems, search rankings, and labor market analyses. As job markets evolve, the increasing complexity of job titles and descriptions necessitates sophisticated models that can effectively leverage intricate relationships within job data. Traditional text classification methods often fall short, particularly due to their inability to fully utilize the hierarchical nature of industry categories. To address these limitations, we propose a novel representation learning and classification model that embeds jobs and hierarchical industry categories into a latent embedding space. Our model integrates the Standard Occupational Classification (SOC) system and an in-house hierarchical taxonomy, Carotene, to capture both graph and hierarchical relationships, thereby improving classification accuracy. By embedding hierarchical industry categories into a shared latent space, we tackle cold start issues and enhance the dynamic matching of candidates to job opportunities. Extensive experimentation on a large-scale dataset of job postings demonstrates the model's superior ability to leverage hierarchical structures and rich semantic features, significantly outperforming existing methods. This research provides a robust framework for improving job classification accuracy, supporting more informed decision-making in the recruitment industry.