JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer
Nanjing University of Science and Technology（南京理工大学）；Nanjing University（南京大学）；BOSS Zhipin（BOSS直聘）；Hong Kong Polytechnic University（香港理工大学）；The Hong Kong University of Science and Technology（香港科技大学）
Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent recruitment. In real-world management scenarios, the available JD-user records always consist of JDs, user profiles, and click data, in which the user profiles are typically summarized as the user's skill distribution for privacy reasons. Although existing sophisticated recommendation methods can be directly employed, effective recommendation still has challenges considering the information deficit of JD itself and the natural heterogeneous gap between JD and user profile. To address these challenges, we proposed a novel skill-aware recommendation model based on the designed semantic-enhanced transformer to parse JDs and complete personalized job recommendation. Specifically, we first model the relative items of each JD and then adopt an encoder with the local-global attention mechanism to better mine the intra-job and inter-job dependencies from JD tuples. Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage, and then combine the user profiles for final prediction in the ranking stage. Consequently, we can embed rich contextual semantic representations for learning JDs, while skill-aware recommendation provides effective JD-user joint representation for click-through rate (CTR) prediction. To validate the superior performance of our method for job recommendation, we present a thorough empirical analysis of large-scale real-world and public datasets to demonstrate its effectiveness and interpretability.
DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation
School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；Institutes of Physical Science and Information Technology, Anhui University（安徽大学物理与信息技术研究院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?
Institut de Mathématiques de Toulouse（图卢兹数学研究所）；CNRS（法国国家科学研究中心）；Université de Toulouse（图卢兹大学）；Institut de Recherche en Informatique de Toulouse（图卢兹计算机科学研究所）；AKKODIS group（AKKODIS集团）
Salience and Market-aware Skill Extraction for Job Targeting
在LinkedIn，我们希望为全球劳动力中的每个人创造经济机会。为了实现这一目标，LinkedIn提供了一个反应式的求职系统，以及一个主动式的你可能感兴趣的工作（JYMBII）系统，以匹配最好的候选人与他们的梦想工作。开发这些系统的最具挑战性的任务之一是正确地提取重要的技能实体从职位发布，然后与匹配的属性的目标成员。在这项工作中，我们表明，常用的基于文本的\emph{salience and market-agnostic}技能提取方法是次优的，因为它只考虑技能提及，忽略了技能的显着水平及其市场动态，即，市场供求关系影响技能的重要性。为了解决上述缺点，我们提出了\model，我们部署的\emph{salience and market-aware}技能提取系统。所提出的\model ~在提高在线工作推荐（JYMBII）（$+1.92\%$工作申请）和针对工作海报的技能建议（$-37\%$建议拒绝率）方面显示了有希望的结果。最后，我们提出的案例研究显示有趣的见解，对比传统的技能识别方法和建议\model ~从职业，行业，国家和个人的技能水平。基于上述有希望的结果，我们部署了\model ~online，以提取LinkedIn上所有$20$ M职位发布的工作定位技能。
Generative Job Recommendations with Large Language Model
Career Science Lab, BOSS Zhipin（BOSS直聘职业科学实验室）；University of Science and Technology of China（中国科学技术大学）；The Hong Kong University of Science and Technology（香港科技大学）
