Job mobility prediction is an emerging field with significant benefits for organizations and individuals, including enhanced job recommendations and career planning. Existing approaches leveraging textual information primarily rely on traditional neural network embedding methods, which are limited in capturing relationships between job-related entities at the semantic level. While Large Language Models (LLMs) offer greater potential by utilizing extensive background knowledge to better understand latent patterns in career trajectories, pure LLMs often struggle with hallucination issues due to insufficient domain-specific knowledge. To bridge this gap, we propose a knowledge graph-enhanced LLM framework, S mart C areer P redictor ( SCP ). Our approach introduces three key innovations: (1) a job-related sub-knowledge graph retrieval mechanism to retain the most relevant job transition patterns, addressing the hallucination problem by providing enriched background knowledge; (2) an entropy-based retrieval score to alleviate graph imbalance issues by ensuring a more equitable representation of career entities; and (3) a multi-modal adapter to effectively integrate textual and graph-based information, enabling more accurate and context-aware job mobility predictions. Experimental results on two real-world career datasets show that our model significantly outperforms state-of-the-art baselines in next career hop prediction. The code for this paper is available at https://github.com/cuishuting/SCP .
Cui et al. (Sat,) studied this question.