ConFit v2: Improving Resume-Job Matching using Hypothetical Resume Embedding and Runner-Up Hard-Negative Mining

• URL: http://arxiv.org/abs/2502.12361v2
• Date: Wed, 19 Feb 2025 19:18:31 GMT
• Title: ConFit v2: Improving Resume-Job Matching using Hypothetical Resume Embedding and Runner-Up Hard-Negative Mining
• Authors: Xiao Yu, Ruize Xu, Chengyuan Xue, Jinzhong Zhang, Zhou Yu,
• Abstract summary: We introduce ConFit v2, an improvement over ConFit to tackle the sparsity problem. We propose two techniques to enhance the encoder's contrastive training process. We evaluate ConFit v2 on two real-world datasets and demonstrate that it outperforms ConFit and prior methods.
• Score: 15.63898598630676
• License:
• Abstract: A reliable resume-job matching system helps a company recommend suitable candidates from a pool of resumes and helps a job seeker find relevant jobs from a list of job posts. However, since job seekers apply only to a few jobs, interaction labels in resume-job datasets are sparse. We introduce ConFit v2, an improvement over ConFit to tackle this sparsity problem. We propose two techniques to enhance the encoder's contrastive training process: augmenting job data with hypothetical reference resume generated by a large language model; and creating high-quality hard negatives from unlabeled resume/job pairs using a novel hard-negative mining strategy. We evaluate ConFit v2 on two real-world datasets and demonstrate that it outperforms ConFit and prior methods (including BM25 and OpenAI text-embedding-003), achieving an average absolute improvement of 13.8% in recall and 17.5% in nDCG across job-ranking and resume-ranking tasks.

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