FitCLM: LLM-Augmented Candidate-aware Cross-view Contrastive Learning for Person-Job Fit

The widespread adoption of online recruitment platforms has highlighted the challenge of achieving efficient and personalized person-job fit. Existing approaches focus on interaction patterns or textual content but fail to leverage the rich relationships among candidates, thus limiting their ability to capture the full spectrum of candidates' characteristics. In this paper, we propose a novel model, FitCLM, which integrates graph representation learning and large language models (LLMs) to enable a comprehensive understanding of candidate features. FitCLM first constructs a candidate relationship graph based on historical recruitment data, uncovering potential connections and similarities among candidates. It then utilizes LLMs to process historical job-candidate matching data, producing fine seman-tic representations. Finally, through self-supervised dual-view ranking optimization, FitCLM facilitates collaborative learning between the interaction graph and the relationship graph, while cross-modal contrastive learning aligns graph-based and LLM-generated embeddings. Extensive experimental results demonstrate that FitCLM outperforms state-of-the-art collaborative filtering and content-based baseline methods on NDCG@5 and MRR@5 metrics across two real-world recruitment datasets, with notable advantages in data-sparse scenarios. This study offers new insights into achieving accurate and efficient person-job fit, highlighting the significant potential of LLMs for generating contextual information from recruitment data.