ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning

• URL: http://arxiv.org/abs/2401.16349v1
• Date: Mon, 29 Jan 2024 17:55:18 GMT
• Title: ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning
• Authors: Xiao Yu, Jinzhong Zhang, Zhou Yu
• Abstract summary: We tackle the sparsity problem using data augmentations and a simple contrastive learning approach. ConFit first creates an augmented resume-job dataset by paraphrasing specific sections in a resume or a job post. We evaluate ConFit on two real-world datasets and find it outperforms prior methods by up to 31% and absolute in nDCG@10 for ranking jobs and ranking resumes, respectively.
• Score: 20.599962663046007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A reliable resume-job matching system helps a company find 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 records in resume-job datasets are sparse. Different from many prior work that use complex modeling techniques, we tackle this sparsity problem using data augmentations and a simple contrastive learning approach. ConFit first creates an augmented resume-job dataset by paraphrasing specific sections in a resume or a job post. Then, ConFit uses contrastive learning to further increase training samples from $B$ pairs per batch to $O(B^2)$ per batch. We evaluate ConFit on two real-world datasets and find it outperforms prior methods (including BM25 and OpenAI text-ada-002) by up to 19% and 31% absolute in nDCG@10 for ranking jobs and ranking resumes, respectively.

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