The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process. Predominantly, current job recommendations deploy either collaborative filtering or person-job matching strategies. However, these models tend to operate as "black-box" systems and lack the capacity to offer explainable guidance to job seekers. Moreover, conventional matching-based recommendation methods are limited to retrieving and ranking existing jobs in the database, restricting their potential as comprehensive career AI advisors. To this end, here we present GIRL (GeneratIve job Recommendation based on Large language models), a novel approach inspired by recent advancements in the field of Large Language Models (LLMs). We initially employ a Supervised Fine-Tuning (SFT) strategy to instruct the LLM-based generator in crafting suitable Job Descriptions (JDs) based on the Curriculum Vitae (CV) of a job seeker. Moreover, we propose to train a model which can evaluate the matching degree between CVs and JDs as a reward model, and we use Proximal Policy Optimization (PPO)-based Reinforcement Learning (RL) method to further fine-tine the generator. This aligns the generator with recruiter feedback, tailoring the output to better meet employer preferences. In particular, GIRL serves as a job seeker-centric generative model, providing job suggestions without the need of a candidate set. This capability also enhances the performance of existing job recommendation models by supplementing job seeking features with generated content. With extensive experiments on a large-scale real-world dataset, we demonstrate the substantial effectiveness of our approach. We believe that GIRL introduces a paradigm-shifting approach to job recommendation systems, fostering a more personalized and comprehensive job-seeking experience.

翻译：在线招聘服务的快速发展推动了推荐系统在求职流程中的应用。当前主流的工作推荐方法主要采用协同过滤或人岗匹配策略。然而，这些模型往往作为"黑箱"系统运行，难以向求职者提供可解释的指导。此外，传统的基于匹配的推荐方法仅限于检索和排序数据库中已有的职位，限制了其作为综合职业AI助手的潜力。为此，本文提出GIRL（基于大语言模型的生成式工作推荐）这一创新方法，灵感源于大语言模型（LLMs）领域的最新进展。我们首先采用监督微调（SFT）策略，指导基于LLM的生成器根据求职者的简历（CV）生成恰当的职位描述（JD）。在此基础上，我们训练一个评估简历与职位描述匹配度的奖励模型，并采用基于近端策略优化（PPO）的强化学习（RL）方法对生成器进行进一步微调。这使得生成器能够与招聘人员的反馈对齐，输出结果更契合雇主偏好。特别值得关注的是，GIRL作为一种以求职者为中心的生成式模型，无需候选集即可提供工作建议。该能力还能通过生成的补充特征增强现有工作推荐模型的性能。通过在大型真实世界数据集上的广泛实验，我们验证了方法的显著有效性。我们相信GIRL为工作推荐系统引入了一种范式转换的方法，能够促进更个性化、更全面的求职体验。