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.

翻译：大语言模型（LLMs）在自然语言处理任务中引发革命，展现出跨领域的卓越能力。然而，其在职位推荐行为图理解方面的潜力仍鲜有探索。本文聚焦于揭示大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）申请。我们提出一种新颖框架，通过利用大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，并揭示潜在模式与关联。具体而言，我们首次提出元路径提示构建器，借助LLM推荐器理解行为图，并设计相应路径增强模块以缓解基于路径序列输入引入的提示偏差。借助这一能力，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。我们在综合性数据集上评估了该方法的效果，并证明其能提升推荐结果的相关性与质量。本研究不仅揭示了大语言模型尚未被开发的潜力，也为招聘市场中先进推荐系统的开发提供了重要见解。相关成果为自然语言处理领域的研究注入新动力，并为优化求职体验提供了实际应用价值。