ConFit: Improving Resume-Job Matching using Data Augmentation and
  Contrastive Learning
Xiao Yu Jinzhong Zhang Zhou Yu
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.