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Large Language Models (LLMs) have revolutionized natural language processingtasks, demonstrating their exceptional capabilities in various domains.However, their potential for behavior graph understanding in jobrecommendations remains largely unexplored. This paper focuses on unveiling thecapability of large language models in understanding behavior graphs andleveraging this understanding to enhance recommendations in online recruitment,including the promotion of out-of-distribution (OOD) application. We present anovel framework that harnesses the rich contextual information and semanticrepresentations provided by large language models to analyze behavior graphsand uncover underlying patterns and relationships. Specifically, we propose ameta-path prompt constructor that leverages LLM recommender to understandbehavior graphs for the first time and design a corresponding path augmentationmodule to alleviate the prompt bias introduced by path-based sequence input. Byleveraging this capability, our framework enables personalized and accurate jobrecommendations for individual users. We evaluate the effectiveness of ourapproach on a comprehensive dataset and demonstrate its ability to improve therelevance and quality of recommended quality. This research not only shedslight on the untapped potential of large language models but also providesvaluable insights for developing advanced recommendation systems in therecruitment market. The findings contribute to the growing field of naturallanguage processing and offer practical implications for enhancing job searchexperiences. We release the code at https://github.com/WLiK/GLRec.

Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations