Large Language Models (LLMs) have revolutionized natural language processing tasks, demonstrating their exceptional capabilities in various domains. However, their potential for behavior graph understanding in job recommendations remains largely unexplored. This paper focuses on unveiling the capability of large language models in understanding behavior graphs and leveraging this understanding to enhance recommendations in online recruitment, including the promotion of out-of-distribution (OOD) application. We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs and uncover underlying patterns and relationships. Specifically, we propose a meta-path prompt constructor that leverages LLM recommender to understand behavior graphs for the first time and design a corresponding path augmentation module to alleviate the prompt bias introduced by path-based sequence input. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users. We evaluate the effectiveness of our approach on a comprehensive dataset and demonstrate its ability to improve the relevance and quality of recommended quality. This research not only sheds light on the untapped potential of large language models but also provides valuable insights for developing advanced recommendation systems in the recruitment market. The findings contribute to the growing field of natural language processing and offer practical implications for enhancing job search experiences. We release the code at https://github.com/WLiK/GLRec.

翻译：大型语言模型（LLMs）已彻底革新自然语言处理任务，展示了其在多个领域的卓越能力。然而，它们在职位推荐中对行为图理解的潜力尚未得到充分探索。本文旨在揭示大型语言模型理解行为图的能力，并利用这种理解增强在线招聘推荐，包括促进分布外（OOD）应用推广。我们提出了一种新颖框架，利用大型语言模型提供的丰富上下文信息和语义表征来分析行为图，挖掘潜在模式与关联。具体而言，我们设计了一个元路径提示构建器，首次借助LLM推荐器理解行为图，并配套设计路径增强模块，以缓解基于路径序列输入引入的提示偏差。通过利用这一能力，我们的框架能够为个体用户提供个性化且精准的职位推荐。我们基于综合性数据集评估了方法有效性，证明了其提升推荐结果相关性与质量的能力。本研究不仅揭示了大型语言模型的未开发潜力，还为招聘市场中先进推荐系统的开发提供了宝贵见解。研究结果有助于推动自然语言处理领域的发展，并为优化求职体验提供了实践启示。我们已将代码开源至 https://github.com/WLiK/GLRec。