mlsanityData to 7 Oct 2026

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

arXiv 2401.16349 ↗First posted 29 Jan 2024cs.CL cs.CYcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 36 of its 65 references that are in the map. First posted on arXiv 29 Jan 2024.

Builds on

36 of 65 references.

References · 36

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24 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026An Efficient Long-Context Ranking Architecture With Calibrated LLM Distillation: Application to Person-Job Fitcited by 1