The increasing reliance on online recruitment platforms coupled with the
adoption of AI technologies has highlighted the critical need for efficient
resume classification methods. However, challenges such as small datasets, lack
of standardized resume templates, and privacy concerns hinder the accuracy and
effectiveness of existing classification models. In this work, we address these
challenges by presenting a comprehensive approach to resume classification. We
curated a large-scale dataset of 13,389 resumes from diverse sources and
employed Large Language Models (LLMs) such as BERT and Gemma1.1 2B for
classification. Our results demonstrate significant improvements over
traditional machine learning approaches, with our best model achieving a top-1
accuracy of 92\% and a top-5 accuracy of 97.5\%. These findings underscore the
importance of dataset quality and advanced model architectures in enhancing the
accuracy and robustness of resume classification systems, thus advancing the
field of online recruitment practices.

通过使用大规模数据集和大型语言模型，在线招聘平台的依赖性、简历分类的准确性和网络招聘实践的发展方面，我们展示了一种全面的简历分类方法，取得了显著的改进和进步。

ResumeAtlas：基于大规模数据集和大型语言模型的简历分类再探索

ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets  and Large Language Models

Person-job fit is an essential part of online recruitment platforms in
serving various downstream applications like Job Search and Candidate
Recommendation. Recently, pretrained large language models have further
enhanced the effectiveness by leveraging richer textual information in user
profiles and job descriptions apart from user behavior features and job
metadata. However, the general domain-oriented design struggles to capture the
unique structural information within user profiles and job descriptions,
leading to a loss of latent semantic correlations. We propose TAROT, a
hierarchical multitask co-pretraining framework, to better utilize structural
and semantic information for informative text embeddings. TAROT targets
semi-structured text in profiles and jobs, and it is co-pretained with
multi-grained pretraining tasks to constrain the acquired semantic information
at each level. Experiments on a real-world LinkedIn dataset show significant
performance improvements, proving its effectiveness in person-job fit tasks.

TAROT 是一个层次化的多任务共同预训练框架，旨在更好地利用结构和语义信息进行信息性文本嵌入，通过针对个人资料和工作的半结构化文本的多层次预训练任务限制获得的语义信息。在真实的 LinkedIn 数据集上进行的实验证明了 TAROT 在人 - 岗位匹配任务中的有效性，实现了显著的性能改进。

TAROT：基于半结构化数据进行多任务预训练的分级框架以实现有效的人 - 岗匹配

TAROT: A Hierarchical Framework with Multitask Co-Pretraining on  Semi-Structured Data towards Effective Person-Job Fit

Online recruitment platforms typically employ Person-Job Fit models in the
core service that automatically match suitable job seekers with appropriate job
positions. While existing works leverage historical or contextual information,
they often disregard a crucial aspect: job seekers' social relationships in
professional networks. This paper emphasizes the importance of incorporating
professional networks into the Person-Job Fit model. Our innovative approach
consists of two stages: (1) defining a Workplace Heterogeneous Information
Network (WHIN) to capture heterogeneous knowledge, including professional
connections and pre-training representations of various entities using a
heterogeneous graph neural network; (2) designing a Contextual Social Attention
Graph Neural Network (CSAGNN) that supplements users' missing information with
professional connections' contextual information. We introduce a job-specific
attention mechanism in CSAGNN to handle noisy professional networks, leveraging
pre-trained entity representations from WHIN. We demonstrate the effectiveness
of our approach through experimental evaluations conducted across three
real-world recruitment datasets from LinkedIn, showing superior performance
compared to baseline models.

本研究旨在将职业网络整合到人职匹配模型中，通过设计一个工作场所异构信息网络（WHIN）和一个上下文社交注意力图神经网络（CSAGNN），来提高在线招聘平台的性能。实验证明了我们的方法优于基准模型。