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ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning (2401.16349v1)

Published 29 Jan 2024 in cs.CL and cs.CY

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 $O(B2)$ 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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Authors (3)
  1. Xiao Yu (66 papers)
  2. Jinzhong Zhang (4 papers)
  3. Zhou Yu (206 papers)
Citations (1)
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