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.

本研究旨在揭示大型语言模型在理解行为图并利用这种理解增强在线招聘中的推荐能力，包括促进分布外应用程序，为此我们提出了一个新框架，该框架利用大型语言模型提供的丰富上下文信息和语义表示来分析行为图并揭示其中的潜在模式和关系，从而实现个性化和准确的工作推荐，并在全面的数据集上评估了我们的方法的有效性。