Recommending suitable jobs to users is a critical task in online recruitment platforms, as it can enhance users' satisfaction and the platforms' profitability. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness. With the rapid development of large language models (LLMs), utilizing the rich external knowledge encapsulated within them, as well as their powerful capabilities of text processing and reasoning, is a promising way to complete users' resumes for more accurate recommendations. However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion. In this paper, we propose a novel LLM-based approach for job recommendation. To alleviate the limitation of fabricated generation for LLMs, we extract accurate and valuable information beyond users' self-description, which helps the LLMs better profile users for resume completion. Specifically, we not only extract users' explicit properties (e.g., skills, interests) from their self-description but also infer users' implicit characteristics from their behaviors for more accurate and meaningful resume completion. Nevertheless, some users still suffer from few-shot problems, which arise due to scarce interaction records, leading to limited guidance for the models in generating high-quality resumes. To address this issue, we propose aligning unpaired low-quality with high-quality generated resumes by Generative Adversarial Networks (GANs), which can refine the resume representations for better recommendation results. Extensive experiments on three large real-world recruitment datasets demonstrate the effectiveness of our proposed method.

翻译：在在线招聘平台中，向用户推荐合适的岗位是一项关键任务，这既能提升用户满意度，也能增加平台的盈利能力。然而，现有岗位推荐方法面临用户简历质量低等问题，这限制了其准确性和实际效果。随着大语言模型的快速发展，利用其蕴含的丰富外部知识以及强大的文本处理与推理能力，是完善用户简历以实现更精准推荐的有效途径。然而，直接利用大语言模型增强推荐结果并非普适方案，因为大语言模型可能存在虚构生成和小样本问题，从而降低简历补全质量。本文提出了一种基于大语言模型的岗位推荐新方法。为解决大语言模型虚构生成的限制，我们从用户自我描述之外提取准确且有价值的信息，帮助大语言模型更好地构建用户画像以完成简历补全。具体而言，我们不仅从用户自我描述中提取显性属性（如技能、兴趣），还从用户行为中推断隐性特征，以实现更准确且有意义的简历补全。尽管如此，部分用户仍因交互记录稀疏而面临小样本问题，这导致模型生成高质量简历的指导信息有限。为应对该挑战，我们提出通过生成对抗网络将低质量简历与高质量生成简历对齐，从而优化简历表示，提升推荐效果。在三个大规模真实招聘数据集上的广泛实验验证了所提方法的有效性。