# Enhancing Job Recommendation through LLM-based Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2307.10747 · HTML: https://mlsanity.com/paper/2307.10747/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2023.

## Built on by (most cited first, 14 of 14)

- [Identifying and Improving Disability Bias in GPT-Based Resume Screening](https://mlsanity.com/paper/2402.01732/) (2024)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review](https://mlsanity.com/paper/2401.01519/) (2024)
- [Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review](https://mlsanity.com/paper/2402.18590/) (2024)
- [Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation](https://mlsanity.com/paper/2504.02458/) (2025)
- [CareerBERT: Matching Resumes to ESCO Jobs in a Shared Embedding Space for Generic Job Recommendations](https://mlsanity.com/paper/2503.02056/) (2025)
- [Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions](https://mlsanity.com/paper/2506.11111/) (2025)
- [Reinforcement Speculative Decoding for Fast Ranking](https://mlsanity.com/paper/2505.20316/) (2025)
- [Web-Browsing LLMs Can Access Social Media Profiles and Infer User Demographics](https://mlsanity.com/paper/2507.12372/) (2025)
- [StaffPro: an LLM Agent for Joint Staffing and Profiling](https://mlsanity.com/paper/2507.21636/) (2025)
- [MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model](https://mlsanity.com/paper/2508.13676/) (2025)
- [Generative Pseudo-Labeling for Pre-Ranking with LLMs](https://mlsanity.com/paper/2602.20995/) (2026)
- [On Reasoning Behind Next Occupation Recommendation](https://mlsanity.com/paper/2604.21204/) (2026)
- [An Interpretable CF-RL-TOPSIS Fusion Model for Skills-Aware Talent Recommendation](https://mlsanity.com/paper/2605.24155/) (2026)

## Abstract (arXiv)

Recommending suitable jobs to users is a critical task in online recruitment platforms, as it can enhance users' satisfaction and the platforms' profitability. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness. With the rapid development of large language models (LLMs), utilizing the rich external knowledge encapsulated within them, as well as their powerful capabilities of text processing and reasoning, is a promising way to complete users' resumes for more accurate recommendations. However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion. In this paper, we propose a novel LLM-based approach for job recommendation. To alleviate the limitation of fabricated generation for LLMs, we extract accurate and valuable information beyond users' self-description, which helps the LLMs better profile users for resume completion. Specifically, we not only extract users' explicit properties (e.g., skills, interests) from their self-description but also infer users' implicit characteristics from their behaviors for more accurate and meaningful resume completion. Nevertheless, some users still suffer from few-shot problems, which arise due to scarce interaction records, leading to limited guidance for the models in generating high-quality resumes. To address this issue, we propose aligning unpaired low-quality with high-quality generated resumes by Generative Adversarial Networks (GANs), which can refine the resume representations for better recommendation results. Extensive experiments on three large real-world recruitment datasets demonstrate the effectiveness of our proposed method.