The rapid development of online recruitment platforms has created unprecedented opportunities for job seekers while concurrently posing the significant challenge of quickly and accurately pinpointing positions that align with their skills and preferences. Job recommendation systems have significantly alleviated the extensive search burden for job seekers by optimizing user engagement metrics, such as clicks and applications, thus achieving notable success. In recent years, a substantial amount of research has been devoted to developing effective job recommendation models, primarily focusing on text-matching based and behavior modeling based methods. While these approaches have realized impressive outcomes, it is imperative to note that research on the explainability of recruitment recommendations remains profoundly unexplored. To this end, in this paper, we propose DISCO, a hierarchical Disentanglement based Cognitive diagnosis framework, aimed at flexibly accommodating the underlying representation learning model for effective and interpretable job recommendations. Specifically, we first design a hierarchical representation disentangling module to explicitly mine the hierarchical skill-related factors implied in hidden representations of job seekers and jobs. Subsequently, we propose level-aware association modeling to enhance information communication and robust representation learning both inter- and intra-level, which consists of the interlevel knowledge influence module and the level-wise contrastive learning. Finally, we devise an interaction diagnosis module incorporating a neural diagnosis function for effectively modeling the multi-level recruitment interaction process between job seekers and jobs, which introduces the cognitive measurement theory.

翻译：在线招聘平台的快速发展为求职者创造了前所未有的机遇，同时也带来了快速准确匹配其技能与偏好的重大挑战。职位推荐系统通过优化用户参与度指标（如点击与申请），显著减轻了求职者的海量搜索负担，取得了显著成效。近年来，大量研究致力于开发有效的职位推荐模型，主要集中于基于文本匹配和基于行为建模的方法。尽管这些方法已取得令人瞩目的成果，但必须指出，招聘推荐可解释性的研究仍处于深度探索不足的状态。为此，本文提出DISCO——一种基于分层解耦的认知诊断框架，旨在灵活适配底层表示学习模型，以实现有效且可解释的职位推荐。具体而言，我们首先设计了一个分层表示解耦模块，以显式挖掘求职者与职位隐含表示中蕴含的分层技能相关因子。随后，我们提出层级感知关联建模，通过层级间知识影响模块与层级对比学习，增强层级内与层级间的信息交互与鲁棒表示学习。最后，我们设计了一个结合神经诊断函数的交互诊断模块，通过引入认知测量理论，有效建模求职者与职位间的多层次招聘交互过程。